International Journal of Medical Engineering and Informatics (92 papers in press)
Classification and Comparison of Malignancy Detection of Cervical Cells Based on Nucleus and Textural Features in Microscopic Images of Uterine Cervix
by Shanthi P.B, Shivani Modi, Hareesha K.S, Sampath Kumar
Abstract: Cervical cancer is one of the major cause of death among women worldwide. Pap smear is the cytology based screening test which is used to detect abnormal cervical cells including pre-cancerous lesions. Accurate classification of Pap smear images is one of the challenging task in medical image processing and its performance can be enhanced by extracting and selecting the well-defined features and classifiers. The irregular chromatin structure is one of the prominent diagnostic feature of abnormal cells. Classification is performed based on the extracted textural features and the benchmark Herlev dataset features. RBF (Radial Basis Function), Polynomial and Sigmoid SVM kernels are used for classification and comparison is performed with the features in benchmark database. Precision, Recall and Accuracy were calculated for all the combinations of the features. The classifier gives promising results when benchmark features are combined with textural features and also benchmark nucleus features with textural features. An effective integration of features for cervical cell classification had given good results for fast abnormal cell detection and primary Pap smear cell image classification.
Keywords: Pap smear; Cervical Cancer; Textural Feature; GLCM; SVM Kernel.
A novel feature set for Bone fixator classification from post-operative x-ray images
by Mrityunjaya V. Latte, Kumar Swamy V, Basavaraj S. Anami
Abstract: The paper presents a novel feature set for classification of x-ray images of bone fixators using artificial neural network. The images are obtained from radiologists. We have considered six types of bone fixators, namely, standard, ring, k-wire, screw, rod and plate. Use of both local and global features, wherein local features are defined in consultation with orthopaedicians. The feature set is reduced based on the classification accuracies of individual features. It is observed that the average accuracies for local, global and their combination are 76.83%, 66.16%and 98.3% respectively. The work finds its application in orthopaedics surgeries assisted by robots and second opinion for surgeons.
Keywords: image classification; artificial neural network; bone fracture fixators.
Non invasive assessment of fractional flow reserve using computational fluid dynamics modeling from coronary angiography images
by A. Udaya Kumar, R. Raghavi, R. Reshma, S.P. Angeline Kirubha
Abstract: Fractional flow reserve (FFR invasive) is measured by measuring pressure differences across a coronary artery stenosis by coronary catheterisation technique. This is a gold standard method of determining the extent of stenosis. This method has some potential complications such as coronary vessel dissection, embolism, and renal failure. This paper presents a method for an assessment of FFR noninvasively, with coronary CT angiography imaging and fluid dynamics modelling. FFR is calculated as the ratio between pressure distal to stenosis and pressure proximal to stenosis of the coronary artery region segmented from CT Angiography image using MIMICS software. ANSYS software is used to determine the FFR (0.73
Keywords: coronary CT angiography; computational fluid dynamics; fractional flow reserve; MIMICS; ANSYS.
An efficient ALO Based Ensemble Classification Algorithm for Medical Big Data Processing
by Saravana Kumar Ramachandran, Manikandan Parasuraman
Abstract: In this paper, we indented to propose a consolidated feature selection and ensemble-based classification strategy to diminish the medical big data. Here, the proposed system will be the joint execution of both the Ant Lion Optimizer (ALO) and ensemble classifier. So as to limit the impact of an imbalanced healthcare dataset, ALO is used for the optimal feature selection process. The optimized feature sets are classified by utilizing the ensemble classification technique. The ensemble classification method uses the diversity of individual classification models to create better classification results. In this paper, the proposed ensemble classification algorithm used the Support Vector Machine (SVM), and Recurrent Neural Network (RNN) classifier and the results of every classifier were consolidated by the majority voting technique. It was watched that the proposed ensemble technique got promising classification accuracy contrasted and other ensemble strategies. This ensemble system can administer datasets, as quick as required giving the imperative help to viably perceive the underrepresented class. The proposed approach will diminish the big medical data precisely and productively. The simulation result shows that the proposed method has better classification when compared with the single classifiers such as Random Forest (RF), SVM and Na
Keywords: Medical big data; Ant Lion Optimizer (ALO); Ensemble classifier; Support Vector Machine (SVM); Recurrent Neural Network (RNN).
Analysis and monitoring of a sensor-pill using DQPSK, with Advanced Virtual Instrumentation.
by Ajay Sharma, Hanuman Prasad Shukla
Abstract: Recording vital parameters of the internal parts of our human body is complex and challenging, so to get the exact or the accurate value we do the approximation of the data. Now to bridge this gap of accurate and approximate, technology has now taken a huge jump, and have come forward with something known as swallowable sensor pill. Although, we can not imagine of swallowing anything other than food items but still it is working. The electronic pill (e-pill) discussed in this model already exists, and works on a complete wireless independent system, on a two-way synchronous DQPSK technique using low carrier frequency of 150300 kHz suited for human body. This small system is incorporated in a small swallow able pill, powered by a special 3v button cell. The received body vital signals, i.e., temperature, SpO2 level, pH value, etc., will be live monitored and analysed using the biomedical toolkit of LabVIEW 8.5, in addition to it a comparative chart of more than one patient can be made for comparative analysis.
Keywords: swallowable; synchronous; DQPSK; incorporated; monitored; analysed; LabVIEW.
Identification of Region of Interest for Assessment of Knee Osteoarthritis in Radiographic Images
by Shivanand Gornale, Pooja Patravali, Prakash Hiremath
Abstract: Osteoarthritis is the most common joint disorder in which smooth apparent on the closures of the bone wears away causing stiffness, swelling along with extreme pain. The assessment of Osteoarthritis in the early stage is most essential which is little difficult and inappropriate. The main objective of the paper is to identify the region of interest i.e. cartilage region for the detection of Osteoarthritis. In the work the database of 1173 Knee x-ray images are collected which are manually classified by two different medical experts as per Kellgren and Lawrence grading system. The histogram of oriented gradient method and Local binary pattern are used for computation. The computed features are classified using decision tree classifier. For the proposed method the accuracy of 97.86% and 97.61% is obtained with respect to Medical Expert-I and Medical Expert-II opinion. The results are promising and competitive which are validated by the medical experts.
Keywords: Osteoarthritis Knee X-ray;Median filter;Region of interest; Histogram of oriented gradients;Local binary Pattern;Decision tree.
Knee pathology diagnosis based on muscle activation intervals detection and the relationship between knee flexion and surface EMG
by Ahlem Benazzouz, Zine Eddine Hadj Slimane
Abstract: Muscle activation interval is an important clinical indicator for muscular disorders diagnosis. In this study, the S transform technique was proposed to detect the muscle activation onset and offset timing during gait and determine the relationship between surface electromyographic signals and knee pathology. The results obtained show that the proposed method achieved the shortest average latency (t_onset=0.015s, t_offset=0.014s) compared to the recent methods: SampEn, TKEO, and Integrated Profile. Moreover, the statistical analysis of activation intervals diversity and correlation between sEMG and knee flexion signals provide that for abnormal cases, the linear relationship is very weak and the activation intervals become more diverse.
Keywords: Knee pathology; muscle activation; onset and offset timing; S transform; surface EMG signals.
Improving the Prediction Accuracy of Low Back Pain using Machine Learning through Data Pre-Processing techniques
by G. Ganapathy, N. Sivakumaran, M. Punniyamoorthy, Tryambak Chatterjee, Monisha Ravi
Abstract: Application of machine learning algorithms in the healthcare industry has been increasing by many folds. Low back pain has caused problems to many persons all around the world. An early treatment or detection of whether a person has the symptoms pertaining to low back pain can help faster medication and treatment of the patient and help them with getting their medical condition degraded. This paper focuses on four different machine learning algorithms viz. SVM, Logistic Regression, K-NN and Na
Keywords: K-NN; Logistic Regression; low back pain; SVM; prediction; Naïve Bayes.
Bone metastatic tumour minimisation due to thermal cementoplasty effect, clinical and computational methodologies
by V.C.C. Oliveira, Elza M. M. Fonseca, J. Belinha, C.C. Rua, P.A.G. Piloto, R.M. Natal Jorge
Abstract: The main objective of this work is to study the thermal effect induced by the bone cement polymerisation, in the metastatic tumour minimisation and to understand the role of such procedure in bone tumour necrosis. Different numerical simulations were produced for different cement sizes introduced in a cortical and spongy bone tumour, with or without an intramedullary nail in titanium. The numerical models were built according to average dimensions of patients obtained from digital conventional radiographs. The finite element results allow to conclude about the high temperature spread effect in bone material. In conclusion, values greater than 45 C were obtained in models without the intramedullary nail system. High quantities of cement produce thermal necrosis in bone with more pronounced effect in depth. The temperature located on the intramedullary nail induces heat transfer along the axial length of the bone, due to the metallic nail, justified by its high thermal conductivity.
Keywords: bone tumour; bone cement; thermal necrosis; bone metastatic tumour; temperature; numerical model; simulation; nail; titanium; transient analysis.
Real-time estimation of hospital discharge using fuzzy radial basis function network and electronic health record data
by Ahmed Belderrar, Abdeldjebar Hazzab
Abstract: Hospital resources are scarce and should be properly distributed and justified. Information about how long patients stays in critical intensive care units can provide significant benefits to hospital management resources and optimal admission planning. In this paper, we propose an approach for estimating intensive care unit length of stay using fuzzy radial basis function neural network model. The predictive performance of the model is compared to others using data collected over 13,587 admissions and 54 predictive factors from five critical units with discharges between 2001 and 2012. The proposed model compared to others demonstrated higher accuracy and better estimations. The three most influential factors in predicting length of stay at the early stage of pre-admission were demographic characteristics, admission type, and the first location within the hospital prior to critical unit admission. We have found about 63% of patients with multiple chronic conditions, stayed significantly longer in hospital. Enabling the proposed prediction model in clinical decision support system may serve as reference tools for communicating with patients and hospital managers.
Keywords: data mining; hospital administration; length of stay; machine learning; prediction model.
A NOVEL METHOD FOR AUTOMATIC IDENTIFICATION OF FOVEA LOCATION AND ITS CENTER IN COLOR RETINAL FUNDUS IMAGES
by Bharati M. Reshmi, Rajesh I S, Bharathi Malakreddy A
Abstract: The identification of fovea region plays a significant role in the detection of Diabetic Maculopathy. The presence of exudates within 500micrometer from fovea center requires laser treatment as it is a sign of sight-threatening. An ophthalmologist can decide over the nature of the treatment depending upon the position of the exudates from the fovea. Hence it is very important to identify fovea and its center with high accuracy. In this work, we have proposed a novel algorithm for identification of fovea location and its center in color retinal fundus images. The novelty of this work aims at an approach where fovea location and its center identification are done excluding blood vessels and OD. Initially, the grid is drawn over the retinal image, then the designed algorithm searches for Region of Interest (ROI) for the candidate fovea region by considering the four coordinate points. Finally, by using dark intensity property of fovea and morphological operations, fovea location and its center is identified. The proposed method is simple, robust and it is tested on a publicly available MESSIDOR database and an accuracy of 97.37% is achieved.
Keywords: Diabetic Retinopathy (DR); Diabetic Maculopathy (DM); Optic Disc (OD); Region of Interest (ROI); Blood Vessels (BVs).
Optic Disc and Optic Cup Segmentation in Retinal Images
by Thamer Al Sariera, Lalitha Rangarajan
Abstract: Automated segmentation of the optic disc (OD) and optic cup (OC) is important for retinal image analysis and retinal diabetic retinopathy systems. For OD segmentation, this paper presents a method done in three steps that combines variance and brightness features of the OD to localize leading to increased accuracy in detecting OD rather than using just one feature. As a first step, the image is divided into non-over lapping windows. Then the brightest window with maximum variance in intensity is selected. Subsequently the Circular Hough Transform (CHT) is applied to get the OD segmentation. OC segmentation,is done in two steps:(i) blood vessels inside the OD are eliminated and (ii) restricted region growing performed to get the OC segmentation.The efficacy of the proposed method is demonstrated using the standard benchmark DRIVE and DIARETDB1 databases and by comparing the results of proposed method and some methods in silico.
Keywords: Optic disc; Optic cup; Retinal image;Diabetic retinopathy; Circular Hough Transform.
A Low-complexity Volumetric Model with Dynamic Inter-connections to Represent Human Liver in Surgical Simulators
by Sepide Farhang, Amir Hossein Foruzan
Abstract: We propose a method for visualization of the human liver to represent nonlinear behavior of the tissue and to preserve the objects volume. Our multi-scale model uses dynamic interconnections to keep the size of the gland. We introduce two new parameters to control the influence of an external force on the non-linear material of the liver. Another novelty in the proposed method is to design a multi-dimension data structure which makes it possible to run our code on conventional CPUs and in real-time. We evaluated the proposed algorithm both quantitatively and qualitatively by synthetic and clinical data. Our model preserved 98.4% and 94.1% of a typical volume in small and large deformation, respectively. The run-time of our model was 0.115 second. Our model preserves the volume of a liver during both small and large deformations, and our results are comparable with recent methods.
Keywords: Medical virtual reality; Mass-spring model; Liver surgical simulator; Volumetric mesh; Multi-scale mesh model.
MR-Brain Image Classification System based on SWT-LBP and Ensemble of SVMs
by Mohammed Khalil, Habib Ayad, Abdellah Adib
Abstract: In this paper, we present an efficient MR (Magnetic Resonance) image
classification system. At the first stage, the brain image is decomposed into
several subbands using Stationary Wavelet Transform (SWT). Then, Local Binary
Patterns (LBP) with reduced histograms are computed for each subband to form
several primary feature vectors. Principal Components Analysis (PCA) followed
by Linear Discriminant Analysis (LDA) are then applied to each primary feature
vector in order to transform them into new lower-dimension feature vectors. The
third stage consists of using an ensemble of Support Vector Machines (SVMs)
in order to build voters and make the final decision on the requested image. The
designed system is evaluated on 255 brain images with Five-fold cross-validation.
Experimental results show that the proposed system achieves a classification rate
of 99.78% which outperforms the existing brain classification approaches.
Keywords: MR-Brain image classification; LBP; SWT; PCA; LDA; Ensemble of SVMs.
A systematic review on Detection and Estimation algorithms of EEG signal for Epilepsy
by Shazia Hasan, Ameya K.Kulakarni, Sebamayee Parija, P.K. Dash
Abstract: Epilepsy is the most common neurological disorder characterised by a sudden and recurrent neuronal firing in the brain. As EEG records the electrical activity of the brain so it helps to detect epilepsy of the subject. Early detection of epileptic seizure using EEG signal is most useful in several diagnoses. So aim of the work is to study and compare the different techniques used for feature extraction and classification algorithm. Epilepsy detection research is oriented to develop non-invasive and precise methods to allow accurate and quick diagnose. In this paper, we present a review of significant researches where we can find most suitable method among existing members to improve computing efficiency and detect epilepsy of the subject efficiently and accurately with lesser computational time. The database which is publicly available at Bonn University is taken.
Keywords: EEG signal; Epilepsy; Seizure detection algorithm; performance analysis; wavelet ; Hilbert transform; EMD.
Electro-pneumatic system for intussusception reduction in children and its application in the pediatric surgery
by Mohamed Ras Lain, Chouaib Daoudi, Mohamed Souilah, Abdelhafid Chaabi, Hichem Choutri
Abstract: Intussusception is an important cause of acute abdomen pain in infants and children that often occurs between 3 and 12 months of age. The early diagnosis of this disease is vital factor to avoid recourse to surgery that can poses a real danger to the infant. Recently, pneumatic reduction (Air / Gas) has emerged as a safe and promising method in the treatment of intussusception. The work presented in this article focuses mainly on the implementation of embedded instrumentation based on pressure sensor to improve the manual pneumatic system, which is currently used by the doctors. Initially, a sensor of the Motorola family (MPX 7050) is used for the acquisition of intestinal pressure. The signal conditioner is based around a specialized amplifier and the processing unit is built around Microchip PIC microcontroller. A keyboard and LCD display were used to introduce and visualize the evolution of intestinal pressure, respectively. The experimental results show the feasibility of the designed prototype in the treatment of intussusceptions.
Keywords: intussusception; pediatric surgery; pneumatic reduction; embedded system; pressure sensor; microcontroller.
Ex vivo experimental and numerical study of stresses distribution in human cadaveric tibiae
by Maria G. Fernandes, Elza M.M. Fonseca, Renato N. Jorge, Maria C. Manzanares-Céspedes
Abstract: The mechanical behaviour of human bone tissue subject to drilling has been recently reviewed due to its increased clinical interest. However, no published data exist regarding stress analysis during the drilling. In this study, an elasto-plastic dynamic FE model of bone drilling was developed using the human cadaveric tibia obtained with a handheld 3D scanner. The FE model was validated with experimental tests and different drilling conditions were simulated in order to study the stresses distribution during the drilling process. The developed FE model was in good agreement when compared with experimental tests. Results suggest that the use of lower drill speed and higher feed-rate lead to a decrease in the stress level of the treated tibial bone. The developed FE model can be used for future studies and cover not only the mechanical behaviour of human tibiae but also the thermal aspects.
Keywords: human tibia; drilling; stresses; numerical model; experimental model; feed-rate; drill speed.
The Impact of Income Level on Childhood Asthma in U.S: A Secondary Analysis Study during 2011-2012
by Jalal AlAlwan
Abstract: Despite the abundance of researches relating children and asthma, the racial/ethnic influence on asthma threat have not been fully explained. The aim was to conduct a consistent and new study on a large-scale nationally representative data, including a minority group that has been usually eliminated form racial/ethnic literature. The 2011-2012 National Survey of Children Health (NSCH) dataset was utilized. Asthma was more prevalent among African American children (22.9%) more than white American children 13.1% (p=<.0001). Analysis of the multivariate model revealed a greater risk of asthma for the black African American children comparatively to white American children (adjusted OR" 0.522 95% CI 0.459-0.595). Our findings indicated that childhood asthma was associated with racial/ethnic status, especially with children with low income level.
Keywords: Childhood Asthma; racial/ethnic influence; National Survey of Children Health; Federal Poverty Level.
Access control to the electronic health records: A case study of an Algerian health organization
by Asma Belaidi, Mohammed El Amine Abderrahim
Abstract: Accessibility to information resources in health systems is a very important aspect. This article is about the protection of medical data and focused primarily on access control in health information systems. It is therefore a question of proposing a rigorous modelling allowing to take care of all the aspects related to the secure management of electronic health record. We proposed in the first time a model to the management of the electronic health record in the context of an Algerian health organization. Based on this modeling and by using Or-BAC model, in a second time, we proposed a model of the access control to this electronic health record. The validation of this model using the MotOrBAC tool allowed us a safe passage to an implementable specification. As a result, we develop a set of simple and effective tools to support this aspect.
Keywords: Electronic health records; Access Control; Or-BAC; MotOrBAC.
A Predictive Model for Identifying Health Trends Among Mori and Pacific People Analysis from 10-years of New Zealand Public Hospital Discharges
by Shaolong Wang, Farhaan Mirza, Mirza Baig
Abstract: Our research was focused on the quality of healthcare services for Māori and Pacific Islanders. We used New Zealand (NZ) public hospital discharges data from 2005 to 2015 for our research. A prediction model has been developed to predict the trends for patients with a specific chronic disease, external injuries and operative procedures based on the previous/historic data. Initial exploration suggests that the service demand increased from 138,656 in 2005 to 163,386 in 2015. We successfully analyzed the diseases with highest incidence rate and key characteristics of this group of patients. Our research concluded with a series of key findings on the disease types including injuries, procedures, and services.
Keywords: Predictive model; hospital discharges; machine learning model; data analysis; machine learning; predictive analysis; healthcare delivery; disease prediction; operative procedures; Māori Population and Pacific Islanders.
An effective algorithm to measure the loss of consciousness degree in epileptic seizure
by BAAKEK YETTOU Nour El Houda, Debbal Sidi Mohammed
Abstract: In this work, a new algorithm is developed to measure the loss of consciousness degrees in normal, pre-ictal, and epileptic seizure cases using bi-spectral analysis. The study is carried out on the electroencephalogram EEG signals; in which 200 records are used as pre-ictal cases, and 100 records are used as epileptic cases. All these cases are compared to 100 normal cases which represent the EEG signal in relaxed and in an awake state with open eyes. The obtained results are very satisfactory and show the efficiency of the proposed algorithm. the unconsciousness degree is very low in normal cases, very high in pre-ictal cases, and varies between high to middle during epileptic seizure cases. The algorithm promising for studying the unconsciousness degree in other cases such as anesthesia and in hypnosis cases.
Keywords: EEG signal; loss of consciousness degree; normal cases; pre-ictal cases; epileptic cases; Bispectral analysis.
R-peak detection for improved analysis in health informatics
by VARUN GUPTA
Abstract: Improvement in R-peak detection of Electrocardiogram (ECG) signal is still not saturated even requires better preprocessing, feature extraction and detection stage. Proper detection of heart diseases using the proposed technique only leads to increase its applications in medical engineering for health informatics. R-peak detection is very important for detecting heart diseases, but the involvement of various types of noises makes its detection too much complex. In this work, discrete wavelet transform (DWT) is used as preprocessing tool and Hilbert transform (HT) is used as a feature extraction tool for spectral estimation (in the form of trajectory pattern). Finally, principal component analysis (PCA) is adopted for reducing feature vectors. Detection of R-peaks is accomplished on the basis of variance obtained by first principal component (PC1). For validating this research work MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) Arrhythmia database has been used. The proposed technique was evaluated in MATLAB environment R2015a. The detection sensitivity (SE), positive predictivity (PP), F-score (F-s) and mean squared error (MSE) are estimated for evaluating the performance of the proposed technique. The proposed technique has resulted into SE of 99.88%, PP of 99.88%, F-s of 99.88%, SNR of 7.60dB and MSE of 0.8131%.
Keywords: Electrocardiogram; medical engineering; health informatics; DWT.
A robust Photoplethysmographic imaging for contactless heart and respiratory rates measurement using a simple webcam
by Djamaleddine DJELDJLI, Fethi BEREKSI REGUIG, Choubeila MAAOUI
Abstract: Video Photoplethysmography has been a resurgence of interest of researchers from different domains of science, driven by the demand of low cost, comfortable, contactless, non-stressful, simple and portable technology for physiological parameters measurements. In this paper, we propose a robust and simple method for remotely measure heart and respiratory activities through video Photoplethysmographic signal recordings using a low-cost webcam. The Video Photoplethysmographic signal is detected from colour video recording of a human face in an ambient light environment. The image and signal processing operations steps are minimized and optimized. Three critical aspects are endorsed during the implementation. These are performances, reduced computational time and low computational complexity. Experimental heart rate, breathing rate, and heart rate variability obtained results on 20 healthy subjects show a high correlation with those obtained using an approved contact sensor. The heart rate error obtained in the proposed method is
Keywords: ambient light; facial images; low-cost webcam; physiological parameters; Photoplethysmography; video recording.
Predicting Treatment Outcome of Spinal Musculoskeletal Pain Using Artificial Neural Networks: A Pilot Study
by Ali Al-yousef, Haytham Eloqayli, Anwar Almoustafa, Mamoon Obiedat
Abstract: Musculoskeletal pain is a heterogeneous condition with multiple risk factors, primary sources that can affect treatment and rehabilitation outcome. In this paper, we developed a prediction model for therapeutic subgrouping of musculoskeletal pain using ANN. A dataset of 27 patients with neck/shoulder pain. Patients received a single injection(0.2 ml) of 0.5% lidocaine at the trigger points.ANN model were used for predicting treatment outcome based on influential pre-treatment variables as inputs. Leave One Out Cross Validation (LOOCV) method was used for validation. The strength of each predicting variable was tested using Multilayer Feed Forward Neural Network with Back Propagation(MFFNN) and LOOCV. Then, the MFFNN prediction model was developed and designed based on the selected variables. Post-treatment endpoint follow-up(4th week VAS) was selected as a good indicator of treatment outcome. Serum vitamin D and ferritin were relatively better predictors of treatment response in the current patient group. ANN obtained 85% prediction accuracy.
Keywords: spine; neurosurgery; pain; ANN; neuron; spinal cord; AI; feature selection,DSS.
An enhanced, efficient, affordable wearable elderly monitoring system with fall detection and indoor localization.
by Ch Vineeth, Anudeep Juluru, Gudimetla Kowshik, Shriram K Vasudevan
Abstract: According to the statistics of NCOA (National Council on Aging) these days rate of death in the old aged people has reached a critical state that, for every 11 seconds an older adult is being treated for a fall and for every 19 minutes a death is reported. Most of the people leave their parents alone in the home and go for their respective jobs. In case of any fall or mishap happens to the elderly who are back at home, they are left unnoticed which may be fatal or lead to incurable disease like hip injuries, hemorrhage, tachyarrhythmia which is approximate to a cardiac arrest or even lead to death. In order to reduce these types of risks faced by elderly people, we designed an affordable IoT wearable product that can monitor their movement, the health of old people which can detect their fall immediately. Falls inside the bathroom may be fatal even for young, healthy people. Most of the elderly persons tend to remove their wearables before entering the bathroom and one cant force them either. In order to detect falls even without wearable band, we designed a smart bathroom which is capable of detecting falls and alerts when a fall occurs. These type of bathrooms can be installed in houses, hotels in order to ensure their customer safety. Deaths due to unnoticed falls inside the bathroom can be prevented by installing our system, which will alert the respective authorities immediately when a fall is detected.
Keywords: Fall detection; Old age support; IoT for medicine; Old aged tracking system; Android App; Bathroom fall; Indoor Localisation.
A new design of real-Time monitoring and spectral analysis of EEG and ECG Signals for epileptic seizure detection
by Boumedyen BELAID, Zine-Eddine HADJ SLIMANE
Abstract: The evolution of telecommunications technology has made significant contributions and advances in medical technology. Most of the time, monitoring and evaluation require the use of more than two signals simultaneously recorded. Simultaneous monitoring of the electrocardiogram (ECG) and the electroencephalogram (EEG) is very useful to have information about general state of health of the patient. In this paper, a novel mono-channel wireless ECG&EEG system for epileptic seizure detection is presented. The system employs analog circuits to acquisition, processing and spectral analysis of ECG and EEG signals simultaneously. Arduino Platform is used to digitize and spectral analysis of signals. A 128x64 Graphic LCD Display module and a Bluetooth module are also used for plotting and transmission of signals. We also propose the magnitude squared coherence (MSC) as an important parameter to calculate in frequency domain the relationship between ECG and EEG signals and use it as a relevant discriminator in seizures and the epilepsies classification.
Keywords: Electrocardiogram (ECG); Electroencephalogram (EEG); Arduino; Bluetooth; 128x64 Graphic LCD Display Module; magnitude squared coherence (MSC); Epileptic seizure detection.
Detection of Dichromacy and Achromatopsia Using LabVIEW
by Kandaswamy A, MALAR ELANGEERAN, Ahilaa T.D., Indrani R.
Abstract: The eyes are undoubtedly the most sensitive and delicate organ we possess, and perhaps the most amazing. We rely on our eyesight more than any other sense. Such a human eye faces numerous problems, one such problem with maximum difficulty is Dichromacy and Achromatopsia i.e. complete color blindness. Color blindness is a condition in which people may have mild to severe difficulty in identifying colors. They may not be able to recognize various shades of colors and, in some cases, cannot recognize colors at all. The retinal cone cells are responsible for color white or gray. Total color blindness is called Achromatopsia. This software proposes vision. The three basic types of color blindness are Red, Green and Blue color blindness-generally called as Dichromacy. Red and Green color blindness is more common whereas Blue color blindness- rare type and people cannot distinguish blue or yellow and both the colors are seen as a color vision test using LabVIEW for the early detection of color blindness and helps to prevent Achromatopsia. This virtual instrument is ideal for mass screening in education institutions, clinics, etc., for the early detection of color blindness.
Keywords: Achromatopsia; Colorblindness; Dichromacy; Ishihara chart.
Performance evaluation of a computational model for brain shift calculation
by Karin Correa, Natividad Bermejo, Oscar Andrés Vivas, José Maria Sabater
Abstract: This article shows a solution for the computation of deformable tissues displacements in the brain shift problem in neurosurgery. In this type of surgery, the brain moves and deforms, changing the pre-surgical reference the surgeon had before the intervention. Among the causes of brain shift are the effect of gravity, loss of cerebrospinal fluid as a consequence of the resection practiced, the effect of the drugs supplied, among others. This document refers to the physical model of this displacement to later simulate them in multiphysics software. A phantom test was constructed by means of hydrogels, imitating the porcine brain tissue, which is subjected to compression along the z axis. The results show that the simulation proposed reproduces the behavior of the real phantom with a high level of accuracy. The application developed may serve in the future to reproduce the total behavior of the brain and thus obtain a better calculation of the brain shift.
Keywords: Neurosurgery; brain shift; neuronavigation; medical robotics; constitutive models; hyperelastic material model; constitutive parameters.
Brainwave entrainment through external sensory stimulus: a therapy for insomnia
by Karuppathal Easwaran, Kalpana R, Srinivasan A.V
Abstract: In this work audio, visual and haptic stimuli are used to improve overall sleep quality for insomnia subjects. Two audio signals at two different frequencies were given to left and right ears. This results in binaural beat signal in delta band. Visual input is also given to two eyes through eye-mask to block the entry of external light. Automated system is developed to give pressure at HT-7 with time and pressure control. This therapy is self-administered by the TEST group (who are diagnosed for insomnia) for a brief period on regular pace. Brain signals are acquired before and after therapy to understand the influence of AVE. Subjects who are not into insomnia (CONTROLs) are also studied for sleep pattern to make baseline comparison. By analysing power spectrum of these signals, results demonstrate that average delta signal power increases by 10% becoming at par with CONTROLs. The test responses are also statistically analysed using Cohens d value. The obtained results demonstrate impact of this therapy as significant changes in quality of sleep. Also, insomnia subjects who are not into oral medicine shows better response, in the sense their rhythmic changes became almost similar to that of CONTROLs with marked increase in REM state duration, reaching 20% of full sleep time, a normally recommended value. Thus this being a drug free therapy could be useful to treat insomnia soon after diagnosis and hence could prove to be more useful to the society.
Keywords: Acupressure; Cohen’s d value; Electroencephalography; Insomnia; Power spectrum; Sleep.
The Fundamental Variable of Stress Detection in Health Information System to Measure Health Workers Current Mental Health
by Bens Pardamean, Wikaria Gazali, Hery Harjono Muljo, Teddy Suparyanto, Bharuno Mahesworo
Abstract: The tension or stress that is experienced by health workers can affect the workers performance which might cause clinical errors while doing medical procedures. Human Resource for Health Information System (HRHIS) is an application that collects, stores and analyses health worker related data, should be utilized for stress level detection. The purpose of this paper is to review the needs to utilize HRHIS as a tool for detecting stress level. To find out the current psychological state of health workers, we used a list of questions related to their mental condition and life satisfaction. The result of this research shows that health workers experience conditions that may cause stress at work and influence their ability to concentrate, sleep quality, and decision-making ability.
Keywords: Stress Level; Human Resource; Hospital Information System; HRHIS.
Classification of Vertebral Fractures in CT Lumbar Vertebrae
by Adela Arpitha, Lalitha Rangarajan
Abstract: The existence of a vertebral fracture (VF) in particular compression fracture indicates osteoporosis and is a sole powerful predictor for the advancement of another osteoporotic fracture. With the number of imaging scans consistently expanding, identifying different cases and grades of osteoporotic fractures are missed by the over-burdened radiologist. The objective of this paper is to automatically segment and classify vertebral body fractures. Individual vertebral body is segmented by feeding preprocessed images to hybrid FCK-means algorithm. The shape features from the segmented output and texture features from the original input image are extracted and fed to an artificial neural network (ANN) which performs multiclass classification of vertebral body compression fractures and its associated fracture grades. Our method resulted in an overall classification accuracy of 93.14% based on Genants scoring for VF. The result concludes that with this approach, the clinicians task in diagnosing fractures is made simpler and also aids in suggesting for further treatment.
Keywords: artificial neural network; ANN; FCKMEANS; vertebral body segmentation; vertebral body fracture classification; CT; shape features; texture features.
Hospitalization characteristics of Metabolic Syndrome patients
by Nimisha Patel, Riddhi Vyas, Shankar Srinivasan, Dinesh Mital
Abstract: Metabolic syndrome is a combination of disorders and in conjunction increases the risk of developing several chronic diseases. This study sought to determine the overall in hospitalization characteristics of metabolic syndrome and non-metabolic syndrome patients. This was a cross sectional study with descriptive analysis from Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) dataset from 2012 to 2014.Compared with Non-metabolic syndrome patients, metabolic syndrome patients length of stay was longer at mean 5.10 days versus mean 4.57 days for non-metabolic syndrome patients. Additionally, total in-hospital charges for metabolic syndrome patients was 30% higher than non-metabolic syndrome patients. Risk of developing metabolic syndrome in female was slightly elevated than in men. Having metabolic syndrome in white ethnic group was high and exhibited substantial differences among different ethnicity. Lower socioeconomic status patients were 37% more prevalent in having metabolic syndrome than the higher income patients.
Keywords: Metabolic syndrome; Non-metabolic syndrome; Hospital cost; Length of stay.
Power Analysis of EEG Bands for Diagnosis of Alzheimer Disease
by Sachin Elgandelwar, Vinayak Bairagi
Abstract: Identifying the early and fundamental stage of Alzheimer Disease (AD) called as Mild Cognitive Impairment (MCI) is needed for better medical care. The basic goal of the current research is to evaluate the electroencephalography (EEG) for the diagnosis of AD and to separate the AD from the normal healthy subjects. The EEG signals has diverse frequency bands which reflects mental functions and memory performance of the humans. The EEG is non-invasive and measurable brain signal, which can be used for detecting the memory functions in the case of AD where there is significant slowdown of brain cognitive functions. The present study is based on analysis of power and frequency of EEG signals, which reflects the connection between specific EEG frequency bands and their relative power (RP). It is observed that relative power of few EEG signal bands is closely linked with the AD staging. Slowing of EEG signals is the main feature found in AD subjects. Such slowing down of EEG is increasing the relative power of delta and theta bands, along with the decreased in the power of alpha and beta bands of EEG. This paper shows the relative power analysis of each band in EEG signals to detect the severity of AD
Keywords: Alzheimer Disease (AD); Mild Cognitive Impairment (MCI); Electroencephalography (EEG); Relative Power (RP); Bump Modelling; Frequency Bands.
Effect of two different bone cements in thermal necrosis when associated to titanium versus carbon nailing for bone metastases a numerical study
by V.C.C. Oliveira, Elza M. M. Fonseca, C.C. Rua, J. Belinha, P.A.G. Piloto, R.M. Natal Jorge
Abstract: The main objective is to study the thermal effect induced by different bone cements associated to intramedullary nails in titanium and carbon, in bone metastases treatment. The thermal necrosis effect of each cement polymerisation was verified to understand the role of such procedures. Numerical models with nailing systems, introduced in a cortical and spongy bone metastasis, were developed aiming to predict the temperature produced by different types and amount of cement polymerisation. The results showed that the polymerisation heat release in all models with a cement mantle filling in around the intramedullary nail and the necrosis largest area was predicted with CMW3. It was verified that CF/PEEK nail and high viscosity Palacos R reduce the heat transfer and the necrosis affected area. This effect could be an advantage for treatment, which aims to keep long-term stability and local metastatic disease control for functional improvement and pain relief.
Keywords: bone cement; bone metastases; thermal necrosis; titanium nail; carbon nail; numerical model; thermal analysis; computational model; intramedullary nail; metastatic lesion.
Modified Fuzzy Clustering based Segmentation through Histogram Combined With K-NN Classification.
by Balan Thamaraichelvi
Abstract: Medical Image analysis plays a vital role in diagnosing the disease accurately in the medical field. Image segmentation is a challenging problem in the field of medical image analysis. In this paper, A modified gaussian kernelized additive bias field clustering based segmentation technique with un-supervised K-NN classification technique has been considered to analyse the Magnetic Resonance (MR) brain images for tissue segmentation and Tumor detection. The accuracy of the proposed segmentation and classification techniques is found to be around 95%. The accuracy and the statistical measures like selectivity and sensitivity are calculated using the fractions: True Negative (TN), True Positive (TP), False Negative (FN) and False Positive (FP).
Keywords: Image segmentation; Histogram based centre initialization; Fuzzy C-Means (FCM); Gaussian Radial basis Kernel Function; Discrete Wavelet Transform (DWT); Principal Component Analysis (PCA); K-NN classification.
Analysis and Prediction of Breast Cancer through Feature Selection and Classification Techniques
by Sivasankar Elango, Sathish Kumar A, Sanjivi J, Balasubramanian P
Abstract: In this modern era, rapid research is being conducted in the field ofrnmedical sciences, with datasets of patients regarding their symptoms and their corresponding disease being readily available to the common man through the Internet. This paper aims to contribute to this boom in the field through the application of data mining and machine learning techniques.We have considered a dataset that has documented the appropriate symptoms of 699 patients and whether they have been diagnosed with breast cancer or not. The dimensions of the dataset were significantly reduced through feature selection techniques including both filter as well as wrapper based techniques. Various classification algorithms,rnwhich includes Naive Bayes, Support Vector Machines, Logistic Regression,rnDecision Tree and Boosting algorithms, were then applied and their accuracies were compared. Boosting algorithm provides the better accuracy compared with base classifiers.
Keywords: Breast Cancer;Classification techniques; Machine Learning; Data Mining; Predictive Modeling.
Feature Subset Selection for Cancer Detection Using Various Rank-Based Algorithms
by B. Surendiran, P. Sreekanth, E. Sri Hari Keerthi, M. Praneetha, D. Swetha, N. Arulmurugaselvi
Abstract: Feature Selection in data mining is the process of identifying the profitable features that are more significant in giving accurate results. Feature selection approaches like Filter method, Wrapper method is used here to get the more significant attributes. These methods generate the list of highly important attributes by using various ranker algorithms like Correlation, Relief-F, Information Gain, Gini Index and classifiers like One R, Support Vector machine, Navy Bayes, Random Tree. In this, we are using ranker methods to perform feature selection on Breast Cancer Analysis. Various experiments have been carried out on Breast Cancer Coimbra data set using different classifiers to predict the accuracy. The crucial attributes are identified using feature selection methods, analysed for both balanced and unbalanced datasets and classified using OneR classifier.
Keywords: Feature Selection; filter and wrapper methods; Breast Cancer ;Ranker Algorithm; balanced dataset.
Diagnosing Angiographic Disease Status with the Aid of Deep Neural Network
by Jayakumari Damarla
Abstract: In this decade, one-third of all global deaths reason as cardiovascular diseases- a report from the World Health Organization (WHO). Early diagnosing conserves human lives from cardiovascular diseases, which is possible through computational techniques. This research intends to identify normal/abnormal conditions of heart diseases appropriately with the aid of the Artificial Intelligence (AI) technique. This research includes Deep Neural Network (DNN) to identify heart conditions adequately. It is evident from the investigation that DNN unveils 93.4% accuracy, which is proficient performance over other employed techniques. The performance of the research evaluates through nine-measures, where the DNN shows the superiority over contest techniques in all performance measures while predicting heart disease conditions.
Keywords: Artificial Intelligence (AI); Deep Neural Network (DNN); Angiographic Disease and Prediction/Classification/Diagnosing.
Implementation of non-contact bed embedded Ballistocardiogram (BCG) signal measurement and valvular disease detection from this BCG signal
by M.A. HAFIZ, Abdullah Mahammed Hashem, Ainul Anam Shahjamal Khan, Md. Hossainur Rashid, Md. Azad Kabir Faruqui
Abstract: Electrocardiogram (ECG) is the most common practice to diagnose cardiac abnormalities. In a traditional system, some ECG leads are connected to the patients chest to detect the electrical performance of the heart. For long term observation, this method creates discomfort for the patient. Non-contact measurement of cardiac performance can alleviate this discomfort. As BCG and valvular diseases both are mechanical phenomena, we conjectured that valvular disease could be diagnosed from non-contact BCG measurement. In this paper, we proposed a non-contact way to determine the valvular diseases of the heart which is favorable for long term observation of the patient. The ballistic force of heart was sensed using a series-connected array of piezoelectric elements embedded in the bed. We collected data from two local famous hospitals. We classified the data using Artificial Neural Network (ANN) and Support Vector Machine (SVM). We collected data from normal persons and persons affected by Mitral and Pulmonary Valve Stenosis. After analyzing the data, we nearly predicted the conditions of the persons. We compared the result using overall accuracy, misclassification rate and fitness. We collected 80 data of normal and valvular disease affected patients. We got the highest test accuracy of 79.12% for SVM technique for decomposition level 1. As this technique is completely new and advantageous, it can lead to a new research area of valvular disease detection.
Keywords: non-contact; ballistocardiogram; electrocardiogram; valvular disease; artificial neural network; supporting vector machine.
Controlled magnetic field influence for reperfusion in aortic blood flow using a unified solution approach
by Ebenezer Ige
Abstract: The haemoglobin content of the blood contains iron constituent which makes body fluid a transport medium suitable for magnetic field-based therapeutic interventions and diagnostic applications. However, in cases of aortic dissection (AD), disturbances occasioned by the changes in cross-section affects the distribution of hematocrit (HTC) in the blood flow suggesting ruptured AD and this could result in ischemic insult and could be determined by the magnitude and direction of interruption of blood flow leading to reperfusion. This condition may affect considerably the allosteric-property of blood flow because of oxygenation sensitivity (OS) impairs imaging procedure in magnetic field-based interventions. In this study, we utilized the Einstein viscosity model to formulate a unified solution coupling wall deformation of AD effect, blood flow and magnetic field in a unified equation. The simulation was undertaken for varying HTC from 60% to 80 % for selected controlled values of magnetic flux; we observed pressure distribution in the region of AD showed rapidly increasing momentum and haemodynamic instability. It could be inferred that the contribution of the local magnetic field is directed to the relaxation of the muscles in the region of aneurysm while maintaining the blood flow at uniform distribution. The appreciation of scaled controlled magnetic effects demonstrated in this work could be applied to the management of critical cardiovascular issues such as necrosis and sepsis in a magnetic field environment.
Keywords: Allostery; magnetic therapy; modeling; unified solution technique; aortic dissection.
Microfluidics Dielectrophoresis Device for Potential Cancer Cell Detection and Separation
by Nur Fatien Najwa Mohamad Narji, Mohd Ridzuan Ahmad
Abstract: Cancer is a leading cause of death that gives a negative impact on all ages and genders worldwide. There are a variety of methods to detect this disease such as CT scanning and Mammography. Even though the current methods have many advantages, however, most of the methods share similar disadvantages such as the detection result takes a long period. Sometimes, the results are not accurate, which can cause overdiagnosis or vice versa. Dielectrophoresis (DEP) is a label-free method, which can be used to obtain the parameters of cell electrical properties such as capacitance, conductivity, and permittivity of the cancer cells. rnIn this study, a device was designed with a pair of electrodes and the main channel with two inlets and two outlets. COMSOL software was adopted to study the flow of the particles in the channel. After that, the COMSOL software was used to run the simulation of the cell properties. The results presented two findings, i.e. the optimal design and dimension of the microfluidic device and cell sorting application. The simulations reveal that the particles were successfully captured by the electrodes and sorted within a specific time. The probability of cell capture and the ability of the electrodes to sort out the cells is about 80%. As for the potential application, DEP can be used as a non- invasive technique to separate the normal cells and cancerous cells, which can lead to early detection as it gives a real-time notification.rn
Keywords: Microfluidic; Simulation; Cell separation; Dielectrophorosis; Cancerous cell.
EFFICIENT TUMOUR DETECTION FROM BRAIN MR IMAGE WITH MORPHOLOGICAL PROCESSING AND CLASSIFICATION USING UNIFIED ALGORITHM
by G. Sethuram Rao, D. Vydeki
Abstract: Brain diseases caused due to malignant are the biggest concern among all the age groups. Studies show that almost 80% of death cases are reported due to presence of malignant tumour. Hence diagnosing brain tumour at an early stage would increase the survival rate. Magnetic resonance imaging (MRI) plays a major role in diagnosing tumours in human brain. However, it is considered to be a time consuming and tedious process which could lead to deviation in the opinion of radiologists. This has led to the development of computer-based automatic extraction of tumour cells from the images obtained by MRI. This paper proposes an efficient tumour detection mechanism from MR images using morphological processing and unified algorithm. A neural network that uses bounding boxes and associated class probabilities detects the
packets of tumour that exist in a full MR image. Simulated results of the proposed technique on the BRATS 2016 dataset show that a detection accuracy of 95.97% is achieved, while reducing the likelihood of false positives. This approach is compared with other detection methods such as DPM and R-CNN and the analysis proves that our method proposed outclasses the other detection methods.
Keywords: terms-magnetic resonance image; brain tumour; thresholding; histogram; segmentation; CLAHE; unified detection; malignant; benign.
PILOT STUDY OF THz METAMATERIAL BASED BIOSENSOR FOR PHARMACOGENETIC SCREENING
by Samla Gauri
Abstract: Introduction: Empirical treatment provided by the clinicians before the pharmacogenomics; study known to be a major reason for morbidity and further severe consequences of adverse; drug reaction. The absence of impeccable information and primary applicable medication; induce the mortality rate associated with particular disease rather than minimize disease, risk,; and complication.; Materials and methods: The introduction of THz metamaterial biosensor to trace biomarker; that induce adverse drug reaction is an ideal thought to overcome drug hypersensitivity; reaction. The biosensor is mainly used to pharmacogenetic screening to study cell behaviour; towards prescribed dosage of drugs. The THz metamaterial biosensor designed in COMSOL; multiphysics based on resonance vibrational frequency and dielectric material property of the; biomarkers.; Conclusion: The difference in resonance frequency of normal cells and biomarkers is used to; trace targeted biomarkers. The THz metamaterial biosensor has great potential as portable; healthcare device for rapid and accurate biomarker analysis as well as diagnosis.
Keywords: Metamaterial biosensor; pharmacogenomics; simulation; COMSOL multiphysics.
Frequency Domain Analysis of Gray Level Intensities for Extraction of Optic Disc in Retinal Images
by Sangita Bharkad
Abstract: Revealing and extraction of Optic Disc (OD) in fundus images is the most important step in automatic screening system of diabetic retinopathy. The algorithm presented in this paper is focused on revealing and extraction of Optic Disc (OD) in fundus images. This algorithm adopts frequency domain approach to focus the OD and mathematical morphology for extraction of the OD. Large pathological signs, bright lesions limit the OD segmentation performance as brightness of both OD and bright lesions is similar. Bright lesions are extracted before localization of the OD using DFT and morphological dilation is used for segmentation of the OD. This method was assessed on standard databases namely: DRIVE, DIARETDB1 and DIARETDB0. This algorithm acceptably recognizes the OD in 255 out of 259 retinal images (98.45%) in 0.75 seconds. 78.986.65 % and 99.3799.58% are the OD segmentation sensitivity and specificity achieved on these three databases. The proposed method demonstrates acceptable robustness on normal and pathological signs retinal images. Focused experimental results reveals the superior performance of presented work with respect to the methods demonstrated in literature.
Keywords: Retinal Image; Optic Disc; DFT; Morphology; Dilation; Segmentation.
Adaptive Neuro-fuzzy based Attention Deficit/Hyperactivity Disorder Diagnostic System
by ANOOP KUMAR SINGH, Deepti Kakkar, Tanu Wadhera, Rajneesh Rani
Abstract: The main purpose of this research paper is to develop a simple automated system for the accurate diagnosis of Attention Deficit/Hyperactivity Disorder (ADHD) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). The designed diagnostic system has two stages- primary and secondary. In the primary stage, a hierarchical fuzzy-based short version of the gold diagnostic tool Connors scale has been implemented to evaluate the behavioral aspects in a fast and simple manner. The secondary stage targets the two main abilities of brain functionality- attention and perception. The determining traits were extracted from ERP components, especially the P300 wave, using peak amplitude and average latency rate. The proposed secondary diagnostic stage is based on Takagi-Sugeno fuzzy inference system and it integrates the features of both artificial neural network and fuzzy logic into a single framework. The system accuracy is 99.3% in classification, i.e., ADHD vs. Normal and 88.78% in severity level (Normal/Low, Medium and High) of ADHD. Thus, the proposed model provides an adaptive and better alternative to ADHD diagnosis.
Keywords: ADHD; Artificial neural network; Fuzzy logic; Backpropagation; ANFIS; Neuro-fuzzy inference system; Event-related potential; FIS; Standalone fuzzy inference system.
MODELING AND ANALYSIS OF KNEE AND HIP JOINTS IN HUMAN BEING
by Bhaskar Kumar Madeti
Abstract: ABSTRACT: The present work aims at developing a representation of all the forces by first drawing the free body diagrams of the knee and hip joints. In order to do force analysis one needs to study knee and hip anatomy. With the aid of MRI Scan data, the moments of the forces are computed so as to solve the equilibrium equations. A 3-dimensional (3-D) finite element analysis is generated to represent the real world situation as closely as possible. The accuracy is improved using image processing commercial software on Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. The analysis was conducted for body weights of 600 N, 1000 N and 1500 N for each of the possible postures during various activities. One important recommendation that can be made from present work is that in order to find proper replacement of human knee and hip joint, one needs to collect CT scan slices and then create 3D models performing F.E analysis by selecting the persons weight. In other words the selected implant must be customized for the patients weight, rather than making the choice by rule of thumb as in common practice in hospitals today.
Keywords: Keywords: CT scan; MRI Scan; 3D model; FE analysis; Knee; Hip.
STUDY OF DEMOGRAPHIC AND RISK FACTORS ASSOCIATED WITH LIVER DISEASES
by Disha Sheth (Kothari), Riddhi Vyas, Shankar Srinivasan
Abstract: ABSTRACT:Liver diseases can be diagnosed by interpreting enzyme abnormality pattern and patient characteristics. Despite growing evidence suggesting different causes, there is a need to explore the risk factors which lead to liver diseases in different population. NHANES Data (2015-2016) was investigated for gender, age, race and country of birth in patients with high liver enzymes values. Different elements were also studied for significant liver disease risk based on odds ratio, 95% confidence interval and relative risk using Fisher Exact Test. Results showed US-born, Non-Hispanic white young males had high values for liver enzymes, demonstrating greater risk in such population for liver diseases. Odds ratio (<1.0); P-value of significance (<0.0001) indicated negligible risk associated with all elements - iron overload, diabetes, smoking, alcohol, blood pressure, total cholesterol, obesity, total protein, albumin and total calcium. Validation studies were also performed using NHANES data (2013-2014), authenticating the results obtained.
Keywords: Liver Diseases; Demographic; Odds Ratio; Relative Risk; Fisher Exact Test.
A Novel UWB Compact Elliptical-Patch Antenna for Early Detection of Breast Cancer in Women with High Mammographic Density
by Amber Khan, Mainuddin , Moin Uddin, Parikshit Vasisht
Abstract: Microwave imaging is one of the emerging technologies for early detection of breast cancer among women having dense Mammographic densities. One of the critical and valuable components of an accurate, effective and compact, involving minimum risk - Microwave Imaging system for early breast cancer detection is an Ultra Wideband (UWB) antenna. A novel, compact elliptical UWB microwave antenna is presented in this research article that might be suitable for early breast cancer detection. The simulation of antenna structure is carried out using HFSS13 FEM based EM software. The simulation results yield better UWB response. The antenna structure provides a wide practical fractional bandwidth of more than 156%. A significant performance factor of the proposed antenna is its ability to provide sufficient gain level for short distance communication. Thus, the proposed antenna is a strong candidate for design and development of microwave imaging system for early detection of breast cancer among women with dense Mammographic densities.
Keywords: Ultra wideband (UWB);Wireless Body Area Network (WBAN);Elliptical-patch; Quality of Service (QoS).
Diagnosing Lung Cancer with the aid of BPN in associate with AFSO-EA
by Rahul Shreyas, Gopika Kumari
Abstract: This work aims at identifying lung cancer into various classes of carcinomas or as a normal-lung, with the aid of an artificial neural network classifier. One thousand input attributes obtained from multiple modality images of the lung, and four output classes are defined. Existing works in the area choose to maximize the accuracy of classification as the primary goal of their researches. However, the reduction of the process complexity has been a relatively untouched area. Few of the available works have tried out the possibilities of reducing the number of input features. This work aims at modeling an optimum Back Propagation Network (BPN) model, by reducing the input feature count and by optimizing the number of neurons in each layer of the BPN classifier without compromising the accuracy. This work incorporates Artificial Fish Swarm Optimization (AFSO) and Evolutionary Algorithm (EA) and proposes a hybrid AFSO-EA for reducing the input feature set. This work also configures a BPN model, where the number of neurons in each hidden layer is optimized using the same hybrid method. The investigation results reveal that the proposed hybrid AFSO-EA technique generates a BPN model, which can achieve 97.5% classification accuracy, with much less computational overhead, than the existing methods.
Keywords: Lung cancer; Backpropagation network (BPN); Levenberg-Marquardt (LM); Artificial Fish Swarm Optimization (AFSO); Evolutionary Algorithm (EA) and hybrid Artificial Fish Swarm Optimization - Evolutionary Algorithm (AFSO-EA).
Operating room scheduling 2019 survey
by Maha TOUB, Omar SOUISSI, Said ACHCHAB
Abstract: Numerous optimization problems in Healthcare have been approachedrnby researchers over the last three to four decades. Hospital logistics - organized and structured to secure patient satisfaction in terms of quality, quantity, time, security and least cost - forms part of the quest for global performance. We provide herein a review of recent study and applications of Operations Research in Healthcare. In particular, we survey work on optimization problems, focusing on the planning and scheduling of operating rooms. The latter is a highly strategic place within the hospital as it requires key medical competence and according to related works surgical sector expenditure represents nearly a third of a hospitalsrnbudget. We analyze recent research on operating room planning and schedulingrnfrom 2008 to 2019; our evaluation is based on patient characteristics, performance measurement, the solution techniques used in the research and the applicability of the research to real life cases. The searches were based on Pubmed, Web of science, sciencedirect and google scholar databases.
Keywords: Operation Research; Healthcare; Operating room; Scheduling;rnPlanning; Optimization; Surgery scheduling; Literature review.
Detection of Abnormality in Breast Thermograms using Canny edge detection algorithm for Thermography Images
by Kumod Gupta, Ritu Vijay, Pallavi Pahadiya
Abstract: Currently research towards cancer is gaining fast attention, as methods to cure
cancer are a holy grail. Among many potential techniques, breast cancer thermography
techniques may come up in saving many lives in the future. The purpose of this paper is
to diagnose breast cancer at preliminary stage using infrared breast thermography. In the
first approach, the thermography image is acquired and conclusions are drawn on the
basis of their symmetry using the histogram, is not appropriate to take decision for
practitioner. In the second approach image is processed and apply algorithms to get good
result. Further, it also helps us to explore those statistical features that effectively
distinguish healthy breast thermograms from that of the thermograms caused by a disease.
Finally, graphical representation of the data corresponding to statistical features for both
the left and right breast of the Healthy and sick Patients breast thermogram has been
made in this paper. The mammography report is carefully examined and compared to
signify any abnormality. The values obtained from asymmetric analysis based on the
abnormality detection system are 94.44% of Sensitivity, 83.33% of Specificity and
88.88% accuracy. This presented work is fruitful for the medical practitioner in early
detect breast cancer.
Keywords: Infrared Radiation thermograms (IRT); Mammography Images; Feature Extraction; Malignant; Benign; Region of interest (ROI);.
An automatic ECG arrhythmia diagnosis system using Support Vector Machines optimized with GOA and entropy-based feature selection procedure
by Abdullah Jafari Chashmi, Mehdi Chehel Amirani
Abstract: Primary recognition of heart diseases by exploiting computer-aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient combination classification model using Grasshopper Optimization Algorithm (GOA) and support vector machines (SVMs) called GOA-SVM for ECG arrhythmia diagnosis is proposed. In this approach, the combination of Discrete wavelet transform and higher-order statistics is used to feature extraction and the entropy-based feature selection method. The proposed method has been compared with PSO-SVMs and SVM-RBF kernel function for classifying the five classes of heartbeat categories. Our proposed system is able to classify the arrhythmia classes with high accuracy (99.66%). The simulation results show that classification accuracy in SVM-GOA method is better than SVM-RBF and Neural Network classifier.
Keywords: ECG classification; entropy; Grasshopper Optimization Algorithm; higher order statistics.
Protection of Encrypted Medical Image using Consent based Access Control
by Mancy Lovidhas, Maria Celestin Vigila S
Abstract: An outline which defends tolerant details during facts transfer be necessary for medical management systems. On the way to attain safety and confidentiality for facts transfer, a consent based access control system was proposed. It grants the agreement by distributing token to the data client, where the permission can only be created by official client. Thus the information stored inside the data centre can be accessed only when the data requester has the token, which is similar to the token already present inside the data centre. If the confirmation of data centre is valid, the data requester can access the original information of the user. Eventually, the user will be notified by the data centre to deserve that there is no misuse outside consent. The anticipated consent based access control method is compared with existing methods to achieve less time utilization and low computational overhead.
Keywords: Consent ; Authorization ; Data Requester ; Data Center ; Data Provider.
An Innovative Hearing-Impaired Assistant with Sound Localization and Speech to Text Application
by Balaganesh Baskar, B.V. Damodar, R. Dharmesh, K.R. Tharunkarthik, K.V. Shriram
Abstract: According to the statistics of World Health Organization (WHO), there are about 466 Million people (About 5% of the total population worldwide) are hearing impaired and 34 million of them are children. It is estimated that by the 2050, there will be almost 900 Million people suffering from hearing disability. In India, there are 63 Million people with hearing impairment. Hearing aid prices range from ?20,000 for a basic device to ?2,50,000 for a premium hearing aid. People with hearing disabilities should not have to spend so much money to enable a sense that normal people take for granted. One of the main problems faced by a deaf person is that they find it difficult to have casual conversations because it is hard for them to follow what others are speaking. This can be addressed simply using a mobile application. We present a frugal and affordable system that could show the direction of the speaker along with the speech in text format in real-time. This can be achieved by Sound-Localization and Speech-to-Text conversion. Sound-Localization is a technique used to identify the direction of the source of the sound. There are Speech-to-Text tools that can generate text from a speech in real-time.
Keywords: Sound Localization; Android Application; Simple Conversation Application; Speech to Text.
An efficient AR modeling based Electrocardiogram signal analysis for Health Informatics
by VARUN GUPTA
Abstract: Today health informatics not only require correct, but also timely diagnosis much before the occurrence of critical stage of the underlying disease. Electrocardiogram (ECG) is one such non-invasive diagnostic tool to establish an efficient computer-aided diagnosis (CAD) system. In this paper, autoregressive (AR) modeling is proposed that is an efficient technique to process ECG signals by estimating its coefficients. In this paper, two parameters viz. Atrial Tachycardia (AT), and Premature Atrial Contractions (PAC) are considered for evaluating the performance of the proposed methodology for a total of 17 recordings (6 real time and 11 from MIT-BIH arrhythmia database). As compared to K-Nearest Neighbor (KNN) and Principal Component Analysis (PCA) with AR modeling [ also known as Yule-Walker (YW) and Burg method], KNN classifier coupled with Burg method (i.e. Burg+KNN) yielded good results at model order 9. A sensitivity (S_e) of 99.95%, specificity(Sp or PPV) of 99.97%, detection error rate (DER) of 0.071%, accuracy(Acc) of 99.93% and mean time discrepancy (MTD) of 0.557 msec are obtained. Consistent higher values of all the performance parameters can lead to the development of an autonomous CAD tool for timely detection of heart diseases as required in health informatics.
Keywords: ECG; AR coefficients; Atrial Tachycardia; Premature Atrial Contractions; KNN classifier; PCA classifier; Yule-Walker; Burg method.
A Supervised Learning Model for Medical Appointments No-Show Management
by Ines Ferreira, André Vasconcelos
Abstract: A no-show is a phenomenon that leads to an efficiency decrease in various sectors, including in the health care sector. This research proposes the usage of supervised learning techniques to predict medical appointments no-shows occurrence and to find patient replacements to fulfil last-minute vacancy slots. The prediction is performed using a classification algorithm that computes the probability of no-show for each patient based on features that have shown to influence his or her decision, such as the waiting time, the day of the appointment and the number of previous no-shows, among others. The features are extracted from two distinct healthcare datasets. To reduce the occurrence of no-shows, the system sends reminders and then, the prediction of no-show is performed days before each appointment, so that there is still time to find a replacement, if necessary. In order to select the most suitable classification algorithm, a 10-fold cross validation is used to perform a comparative analysis among the most used algorithms applicable to this type of classification problems, namely Logistic Regression, k-Nearest Neighbors, Random Forests and Gradient Boosting. This research uses four metrics to assess the algorithms performance, including accuracy, precision, recall and f1-score. The Gradient Boosting algorithm proved to have the best performance in estimating no-shows.
Keywords: No-show; Health Care; Supervised Learning; Classification Algorithms; Cross Validation.
Sentiment Analysis of an Epidemic: A case of Nipah Virus in India
by Jayan V, Sreejith Alathur, Rajesh R. Pai
Abstract: Data in social media and other news media can have an impact on the decision-making process of the Government and the citizen if properly examined. The mode and pace of dissemination in both media leads to an increase in the delivery of misinformation. This affects the economy of the country and people's mental health. The government must formulate the required measures to counter the proliferation of fake messages and disinformation in the media, which would otherwise lead to an unnecessary burden. Regulation of health communication during the period of epidemic is important, as it has an effect on the mental health of users of the media. The study assesses the emotions of health communication in social media and online news media in the context of the Nipah Epidemic in India during 2018.
Keywords: Fake News; Nipah; Sentiment Analysis; Social media; psycho-linguistics; neuro-linguistics; misinformation; depression; anxiety.
Hierarchical Cluster Analysis of the Morbidity and Mortality of COVID-19 across 206 Countries, Territories and Areas
by Donald Douglas Atsa'am, Ruth Wario
Abstract: This research deployed the agglomerative hierarchical clustering to extract clusters from the coronavirus disease 2019 (COVID-19) data based on the morbidity and mortality of the novel virus across 206 countries, territories and areas. As of 2nd April, 2020, a total of 896,475 confirmed cases were reported across the world. Three clusters were extracted from the data on the bases of morbidity and mortality of COVID-19. These include: low-confirmed-cases, low-new-cases, low-deaths and low-new-deaths countries [Cluster 1]; medium-confirmed-cases, low-new-cases, medium-deaths, and medium-new-deaths countries [Cluster 2]; high-confirmed-cases, high-new-cases, high-deaths, and high-new-deaths countries [Cluster 3]. It is recommended that, to contain the pandemic, countries within a cluster should cooperate, share information and learn from mistakes or strategies (as the case may be) of the countries in other clusters. Among other benefits, this can prevent countries within the low-confirmed-cases cluster from progressing to the high-confirmed-cases cluster.
Keywords: COVID-19; morbidity; mortality; hierarchical clustering; data mining.
A new approach based on for controlling the joint movement of drop foot patients
by Mina Lagzian, S. Ehsan Razavi, Hamid Reza Kobravi
Abstract: Stepping is one of significant functions, which needs an appropriate coordination between various joints to be accomplished properly. Drop foot is a gait abnormality that the harmony between joints is disturbed. In this paper, proposed a new fuzzy control model for controlling joint movement of drop foot patients. This method has two advantages over pervious ones. The first one is based on identify kinematic pattern not just statistical works. The second is independent of any mathematical models and formulas. The controlling method is based on identification of both stable and unstable manifolds of basin attraction of a healthy person in order to, how to properly move his/her defective leg. The results indicate that using the proposed fuzzy controlling approach, has lower computations and good convergence.
Keywords: stepping procedure; drop foot; gate analysis; stable and unstable manifolds; absorption platform; saddle points; fuzzy control.
A Secure and Intelligent Real-Time Health Monitoring System for Remote Cardiac Patients
by Maroua Ahmid, Okba Kazar, Laid Kahloul
Abstract: In this paper, we propose an intelligent and secure Internet of things approach for the healthcare system that monitors the patient heart rate in real-time and from any place. Thanks to the agent, the proposed system can predict the critical condition before it even happens and takes fast and apt decisions in an emergency case. Based on the experimentation, the proposed system is convenient, reliable, and ensures data security at a low cost. The proposed algorithm outperforms other algorithms regarding the system's operational efficiency. It is more suitable for devices with power, storage, and processing limitations, such as in IoT devices. Also, agents are the better current technologies for heterogeneous and distributed systems, such as the Internet of things. Moreover, this approach scalability makes it suitable for a broad range of IoT environments, including smart homes, smart cities, dynamic and large-area networks, etc.
Keywords: Healthcare; Agent; ECC EIGamal; Remote Monitoring; Cloud; Internet of Things.
Virtual Reality-Based Real-Time Solution for Children with Learning Disabilities and Slow Learners An Innovative Attempt.
by K.V. Shriram, Pranav B, Saravanan G, Merin K. John, Athira Sasidharan
Abstract: Autism Spectrum Disorder (ASD) is a developmental disorder which can be characterized by social and communication impairments, slow learning, combined with limited interests and repetitive behaviors. It affects as many as one in 59 children and is more prevalent in boys with one in 38 diagnosed with Autism Spectrum Disorder. But due to the social stigma associated with mental health and psychological issues, especially in countries like India, most cases go unreported or symptoms ignored. This project is an attempt to help address this issue by providing a means to assist in diagnosing ASD using telemedicine and also to provide an interactive and effective means of learning for children diagnosed with ASD or children with slight learning disabilities. The system features games that have been proven to be effective with children diagnosed with ASD. The virtual reality which is being speculated to be a powerful tool in helping children with learning difficulties has been used to enhance the effectiveness of these games. The concept of dynamic difficulty is also integrated into the game in order to increase or decrease the challenge of each level depending on the performance of the child which further increases the effectiveness of the game.
Keywords: Autism; Slow Learners; Learning Difficulties; 3D; game; Virtual Reality; Hand Tracking; Interactive; Dynamic difficulty; Adaptive levels; Gesture detection; Leap Motion sensor;.
An Innovative Deep Learning Approach for COVID 19 Detection with X-Ray Images and Infected User tracking through Blockchain
by Vimal Kumar, Shriram K Vasudevan, Nitin Dantu
Abstract: The COVID-19 pandemic has shocked the globe with an enormous number of people infected and a large death toll across several nations. Many people lost their loved ones and 350,000 death toll passed globally. By this time more than five million people have been affected. A deadly virus has many victims but no country could stand out when it comes to producing a vaccine. The virus is so dangerous that it spreads rapidly through human contact and a person who is infected will infect around 600 people a month. It is so fast that more than 50,000 people are affected in one day in some countries and more than 1,000 people die in one day. The present situation is so bleak, and if not contained by social distance, it can get even worse. There are many patients but not enough doctors and hospitals to treat them as the infection grows exponentially. No doctor can examine Chest X-ray in thousands and have fast turnaround. We want to create a solution to reduce the workload on doctors, to easily determine whether a Chest X-ray pneumonia is due to coronavirus or not, so that the rapid spread can be controlled and proper cure could be given to patients. Here we also add the distributed ledger technology called blockchain, which helps in monitoring the patient health data and thus it helps in having the complete history of the patient.
Keywords: Covid 19; Covid 19 with Deep Learning; Deep Learning; Blockchain for Covid 19; Covid 19 with XRAY;.
Analysis of Dermal Activity and Skin Images for Diabetic Kidney Disease
by Valli MN, Sudha Singaram, Kalpana Ramakrishnan, Soundararajan Periasamy
Abstract: Any electrical input to skin, changes the ion concentration in sweat, leading to variations in electro dermal activity (EDA) and hence in skin conductivity. Structurally, the pores and connecting tissues contribute to skin texture. Diabetes leads to micro-vascular complications thus affecting the innervations of C-nerve fibers, thereby skin conductivity and micro texture also changes. Diabetic kidney disease (DKD) is another condition under which hydration level and urea in serum and sweat varies leading to dermal changes. Therefore EDA and microscopic-images are acquired from volunteers catering to normal, diabetic and DKD. Features are extracted after convolving EDA signals with Morlet-wavelet and pre-processing the micro texture image for hair removal and enhancement. An expert system is designed to take these features as input and for broad classification. Result of this study demonstrates the influence of serum urea on skin conductivity and texture, thereby enabling skin based method of diagnosing diabetics and DKD.
Keywords: Electro dermal activity; kidney; feature extraction; artificial neural network.
APPROACHES AND CHALLENGES TO SECURE HEALTH DATA
by Patricia Whitley, Hossain Shahriar, Sweta Sneha
Abstract: As the volume of health data being generated and stored massively, the number of data breaches are also increasing causing concerns among patients and healthcare providers on how to protect data better. This article explores blockchain, machine learning and artificial intelligence as possible technologies to secure healthcare data and some challenges when incorporating them to mitigate against data breaches. The paper also discusses a discussion of the issues surrounding the security of health data and improvement.
Keywords: EHR; Data Security; Blockchain; Machine Learning; Artificial Intelligence.
Impact of wireless technologies on public health: a literature review
by Antonio Conduce, Daniela Di Sciacca, Sergio Sbrenni
Abstract: Introduction: The aim of this review is to evaluate the impact wireless technologies have on the public health in terms of patient safety, quality of care and cost savings. Methods: A systematic review was performed. We ended up analysing 76 papers and found the main applications of wireless technologies on public health in the literature. Results were organized in four different categories, one being a subsequent refinement of the previous one. Results: This study identify and analyses the risks and benefits on public health, highlighting strengths and opportunities, especially for patients in prehospital stage. The most relevant benefits identified are: improving outcomes in time-dependent pathologies and reducing management costs. Conclusion: The adoption of wireless technologies in healthcare is still in a trial stage. A careful evaluation of their impact on the quality and sustainability of health services has to be performed in order to obtain the final approval.
Keywords: Emergency Medicine; Ambulances; Quality of Care; Wireless technology; Equipment and Supplies; Telemedicine; Technology Assessment; Biomedical; Electronic Health Records; Review.
THE EFFECT OF SOCIOECONOMIC FACTORS ON HEALTH-RELATED QUALITY OF LIFE AMONG ADULTS WITH DEPRESSIVE DISORDER IN THE UNITED STATES
by Nesren Farhah, Shankar Srinivasan, Dinesh Mital, Frederick Coffman
Abstract: Using data from the Behavioral Risk Factor Surveillance System (BRFSS) a study was conducted to determine the effect of socioeconomic factors of education level, marital status, employment status, and income level on the HRQOL outcomes of activity limitation, physical health, and mental health among adults with depressive disorder in the United States. A greater number of adults with high income level, high education level and married were depression free compared to those with low incomes (39.17% vs 6.49%), low education level (30.46% vs 5.8%), and being single (45.35% vs 8.35%). Also, those with depressive disorder suffered greater physical health problems (11.02% vs 7.93%) and mental health problems (12.58% vs 6.26%).
Keywords: Depressive disorder; socioeconomic factors; Health-related quality of life; mental health; physical health; activity limitation.
MSCs-released TGF?1generate CD4+CD25+Foxp3+ expression in T-reg cells of Human SLE PBMC
by Dewi Masyithah Darlan, Delfitri Munir, Agung Putra, Nelva Karmila Jusuf
Abstract: Regulatory T-cell (Treg) defects may cause autoreactivity of both T and B cells leading to autoimmune disease, including in Systemic lupus erythematosus (SLE) disease. Those defects were characterized by decreased expression of CD4, CD25, and FoxP3, thus restoring the Treg expression can reverse autoimmunity into immune tolerance into a normal immune response. Mesenchymal stem cells (MSCs) have immunomodulatory properties to control inflammation milieu, including in SLE inflammation by releasing TGF?1, IL-10, and PGE2, thus MSCs can generate Treg cells. However, the regulation of Treg by MSCs-released TGF?1 in human SLE remains unclear. This study aims to analyze the role of MSCs-released TGF?1 in generating CD4+, CD25+, Foxp3+expression in T-reg cells of human SLE PMBCs. This study used a post-test control group design using the co-culture of PBMCs from SLE patient and human umbilical cord MSCs (hUC-MSC) as the subject. This study was divided into 5 groups; sham, control, and treatment group treated by co-cultured hUC-MSC to PBMCs with ratio 1:1 (T1), 1:25 (T2), and 1:50 (T3) for 72 hours incubation, respectively. The expression of T-reg was assessed by flow cytometry assay, whereas the TGF?1 using Cytometric Bead Array (CBA).This study showed a significant increase in Treg cell expression (P
Keywords: MSCs; TGF?; CD4+CD25+Foxp3+; T-reg; SLE disease.
An Ensemble Framework-Stacking and Feature Selection Technique for Detection of Breast Cancer
by Vikas Chaurasia, Saurabh Pal
Abstract: Breast cancer is the second most common cancer in women worldwide. The machine learning (ML) method is a modern and accurate technique that researchers have recently applied to predict and diagnose breast cancer. In this research article, we developed stack-based ensemble techniques and feature selection methods for the comprehensive performance of the algorithm and comparative analysis of breast cancer datasets with reduced attributes and all attributes. This article uses five-feature selection technique because it affects the overall performance of the model. After applying feature selection method, now we have data set with reduced features as well as all features. We implemented logistic regression on a dataset with all features and a dataset with reduced features. Finally we see that the dataset with reduced features have got improved accuracy.
Keywords: Breast Cancer; KNN; Perceptron; Stacking; Machine Learning; Feature selection; Algorithm; Ensemble techniques; Logistic regression; Sub models.
DEPRESSION CLASSIFICATION AND RECOGNITION BY GRAPH-BASED FEATURES OF EEG SIGNALS
by Ahad Mokhtarpour, Faezeh Bashiri
Abstract: Major depressive disorder(MDD) is one of the main subjects in world health so its diagnosis is important for researchers. Electroencephalography(EEG) is one of effective tools in brain psychological disorders diagnosis which any change in brain function is reflected in signals. By EEG signal analyzing, some disorders like MDD can be recognized. In this paper EEG signals are firstly mapped to four different visibility graphs and several features are extracted from each graphs. Then feature numbers are reduced by principal component analysis(PCA) and depressive and normal classification is done by support vector machine(SVM). In this paper, classification results by combining all four graph features are compared with each graph features individually and the results show that by combining features lower classification error and better accuracy is achieved. The classification accuracy of depression classification by mixed features is 100 percent which means the proposed method can classify all of them correctly.
Keywords: Electroencephalography; major depression disorder; visibility graphs; support vector machine.
Effective Utilization of Multi Median Variance-Independent Component Analysis on Medical Image Denoising
by Arathi Thiruvoth, Rahul Chingamtotatil
Abstract: Image denoising is a significant pre-processing technique that plays a vital role in medical image processing. Image denoising is the process of removing noise from an image and is a trade-off between noise removal and preservation of significant image details. This paper encloses a sparse representation based denoising technique called Multi Median Variance-Independent Component Analysis (MMV-ICA). Investigation evident, the incorporation of MMV ICA reveals superior denoising results over contest techniques under various noise attacks and noise level conditions. The proposed denoising algorithm based on sparse and redundant representations over learned dictionaries. The dictionary is trained using the corrupted image, and after that, the dictionary is adapted to achieve sparse signal representations. MMV-ICA algorithm presented in this paper makes use of a patch-based dictionary creation method. The paper presents the results of the MMV-ICA denoising technique, which are found to be in par with the existing sparse based denoising methods.
Keywords: Image Denoising; Sparse Representation and Multi Median Variance-Independent Component Analysis.
Clinical Decision Support for Early Diagnosis and Intervention in Multiple Sclerosis
by Shankar Srinivasan, Jojy Cheriyan, Dinesh Mital, Riddhi Vyas
Abstract: Multiple Sclerosis (MS) is one of the most common neurological disorders and cause of disability among young adults in North America and Europe1. It is a non-communicable disease with no cure, debilitated by physical and mental impairments2. Recent reports show an increase in the incidence of MS in United States, more than double of the past estimate. The average period to diagnose MS still ranges from 6 months to 3 years. Studies suggest that early diagnosis and intervention can delay the progression of the disease and improve the quality of life4-6. Until today no clinical decision support exists that could be used to assist clinicians in diagnosing MS at an early stage. This study is conducted to assess the need and explore the quantifiable predictors that could be used for helping clinicians in early detection of disease activity. A review of literature followed by a quasi-experimental approach has been done to collect predictors and analyze the trending incidence of MS in United States. This study reports its preliminary analysis by concluding that currently no clinical decision support system (CDSS) exists to diagnose MS at the point of care. Predictors are available to design a clinical decision support tool for Multiple Sclerosis at the point of care that can help clinicians in the early diagnosis and intervention.
Keywords: Multiple Sclerosis; Health Outcomes; CDSS; Decision Making; Diagnosis and Treatment.
Classification and signal processing analysis
Of The pathological electromyogram signal (EMG)
by Mokdad Aicha, Debbal Sidi Mohammed El Amine, Meziani Fadia
Abstract: The objective of this ongoing study is to introduce electromyography signal (EMG) in time-frequency representation (TFR) applying spectrogram with optimized window size where four features were extracted. In order to qualify or not the capability of spectrogram features in separating healthy and amyotrophic lateral sclerosis (ALS) pathology, three useful classifiers namely support vector machine (SVM), linear discriminate analysis (LDA), K-Nearest Neighbor (KNN) are implemented to classify EMG signals.AS result, spectrogram with optimized window size (512 ms) and SVM based on Radial basis function (RBF-SVM) presents the highest classification accuracy of 92.3% Followed by LDA and KNN with classification accuracy of 90.86% and 83.3% respectively, where the optimized window size of 256 ms is more appropriate. Also, the proposed TFR is able to show the nonstationary variations of sEMG signals. the features exhibit statistically significant difference in the muscle healthy and neuropathic conditions. The combination of RBF based SVM is found to be most accurate (92.3% accuracy) in classifying the conditions with the extracted features based on spectrogram.
Keywords: Amyotrophic lateral sclerosis; spectrogram; classify; support vector machine; linear discriminate
Study of Novel COVID-19 Data using Graph Energy Centrality: A Soft Computing Approach
by Mahadevi S., Shyam S. Kamath, Pushparaj Shetty D.
Abstract: The propagation of the new pandemic COVID-19 is more likely linked to human social relations and activities. A Social Network can be used to describe these human relationships and activities. Understanding the dynamic properties of disease dissemination through diverse Social Networks is critical for effective and efficient infection prevention and control. With the frequent emergence and spread of infectious diseases and their impact on large areas of the population, there is growing interest in modelling these complex epidemic behavior. Such an approach could provide a stronger decision-making method to tackle and control disease. In this paper, a transmission network is developed using the South Korean data, and the study of the network is carried out using Graph Energy Centrality. This measure of centrality allows us to recognize the primary cause of the spread of the virus within the established network by ranking the nodes of the network based on graph energy. The identified primary cause can then be isolated, which can prevent further spread of infection. We have also considered the Novel_Corona_Virus_2019_Dataset from Johns Hopkins University to analyse epidemiological data around the world.
Keywords: Coronavirus; SARS-CoV-2; Centrality Measures; Graph Energy; Data Analysis; Visualization; Social Network Analysis.
Early Diagnosis of Coronary Artery Disease by SVM, Decision Tree Algorithms and Ensemble Methods
by Marziye Narangifard, Hooman Tahayori, Hamid Reza Ghaedsharaf, Mehrdad Tirandazian
Abstract: Heart diseases are considered to be one of the main causes of death around the world. The most reliable method for heart disease diagnosis is angiography, which is costly, invasive and has the risk of death. This study applies data mining techniques to construct a heart disease diagnosis predictive model. In this study, variations of Decision Tree (DT), Support Vector Machine (SVM) and voting algorithms are applied on UCI heart data repository. We show that integrating medical knowledge and statistical knowledge as well as fine tuning the related parameters of the models lead to an effective heart disease diagnosis model. We use two methods for implementing the proposed model. First, we use K-fold cross validation to create the model. The obtained results demonstrate that, voting algorithm and Random Forest, respectively with the accuracy of 86.42% and 85.71%, in comparison with other existing methods can more accurately identify patients with heart diseases. In the second modelling method, we shuffle dataset then split it into two datasets as Train/Validation and Test datasets. We use K-fold cross validation on Train/Validation dataset and then calculate the accuracy of the model with Test dataset. The results of this method demonstrate that, voting algorithm and random forest respectively with the accuracy of 87.5% and 90.0%, in comparison with other existing methods perform well in identification of patients with heart diseases.
Keywords: Data mining; Machine Learning; Decision Tree; Support vector machine; Voting; Random Forest; Forest PA; Heart disease; UCI Dataset.
Design of Protective Vessel and Irrigation System for an Organ-on-Chip Device
by Esmeralda Zuñiga
Abstract: The vascular system has many nutritional roles including the absorption, distribution, and excretion of compounds and determines the osmotic interchange and dynamics of drugs and therapeutics. Currently, Biomedical Engineering develops new devices that allow mimicking any kind of process in the biomedical area. In this work, we design and simulate the external and internal structure of an Organ On Chip (OOC) device, which mimics the nutrient irrigation system, as well as its protection and management. The device was generated with the computer-aided design software (CAD), SolidWorks
Keywords: Organ On Chip; Vessel system; Computer-Aided Design; 3D printing; Flow Simulation; Biomedical device.
A neural network model for preeclampsia prediction based on risk factors
by Masoumeh Mirzamoradi, Atefeh Ebrahimi, Ali Ameri, Masoumeh Abaspour, Hamid Mokhtari Torshizi
Abstract: This study proposes a risk factor-based neural network model for preeclampsia prediction during the second trimester of pregnancy. A total of 320 women giving birth (160 normal delivery, 160 with preeclampsia) at Mahdieh Gynecology Hospital during 2018-2019, were inquired for 13 risk factors. Data from 85% of the subjects (selected randomly) were employed to train the network, and data from the remaining subjects were used to test the performance of the model. This process was repeated 100 times and the average results were determined. The proposed model achieved an accuracy of 83% in classifying the subjects into normal and preeclampsia classes, based on the risk factors input data, with a sensitivity of 83% and a specificity of 82%.
Keywords: Artificial Neural Network; Prediction; Preeclampsia.
Developing Hybrid Fuzzy model for predicting Severity of End Organ Damage of the Anatomical Zones of the Lower Extremities
by Nikolay Aleexevich Korenevskiy, Alexander Vladimirovich Bykov, Riad Taha Al-kasasbeh, Altyn Amanzholovna Aikeyeva, Sofya Nikolaevna Rodionova, Maksim Urievich Ilyash, Ashraf Adel Shaqadan
Abstract: In this research we show methodology to develop hybrid fuzzy decision rules and mathematical models derived to identify rheological indices (D -dimer, leukocytes, platelets, fibrinogen). A fuzzy hybrid model is developed to predict occurrence of complications due to ischemic diseases. Fuzzy hybrid modeling is suitable in health problems because expert judgment can integrated with physical data to build the model. The data set is 400 records of patients with chronic obliterating diseases developed from (2011-2018). rnThe health indicators volumetric flow rate and regional systolic blood pressure are used to define four classes of severity of end organ damage of the lower extremities for which rational treatment regimens are then worked out. rn
Keywords: fuzzy model; critical ischemia of the lower extremities; blood vessel filling; hemostasis.
COVID-19 Detection through Convolutional Neural Networks and Chest X-Ray Images
by Venkata Subbareddy Konkula, Nirmala Devi
Abstract: To break the chain of COVID-19, a powerful and fast screening system is required which identifies the COVID-19 affected cases quickly such that the appropriate measures like Quarantine or treatment can be taken. The traditional Genetics assisted chain reaction test is found to have significant misclassification rate followed by more time consuming. To solve this problem, in this paper we have introduced a new model for COVID-19 detection based on Chest X-Ray (CXR) Images and Convolutional Neural Networks (CNNs). The proposed model is an automatic detection model which considers the CXR image as input and performs an in-depth analysis to discover the COVID-19. The proposed CNN model is a very simple and effective which is composed of five convolutional layers and three pooling layers. Every convolutional layer has different sized filters and different number of filters, which extracts all the possible features from CXR image. Simulation experiments are conducted over a newly constructed dataset based on the publicly available CXR (both COVID-19 and Non-CVOID-19) images. Simulation is done under two phases; 3-class and 2-class and obtained an average accuracy of 92.22% and 94.44% respectively. Thus the average accuracy is measured as 93.33%
Keywords: COVID-19; Deep Learning; CNN; CXR images; Accuracy.
Early diagnosis COVID_19 by computed tomography scan
by Abbood Abbas Abbood
Abstract: COVID_19 is a virus that infects the respiratory system and causes pneumonia, kidney failure, and other health issues. The purpose of early diagnosis of COVID_19 is the fastest time to keep healthy cells for patients. A checkup for COVID_19 has been performed for patients whose ages were between (85-25) years by CT scan and Laboratory-Analysis (PCR) and X-ray and PET/CT scan. CT scan is considered the more clear method for the early diagnosis for COVID_19 because of the production of clear radiographic image quality and high-resolution and three-dimensional of a patient's chest in tow views (anterior-posterior view and the lateral view). In addition to that, a CT scan is more readily and cheaper, and more available in the hospital, also it takes less time to check the patient's chest.
Keywords: computed tomography; COVID_19 ; lung; pneumonia.
Melanoma Classification by 3D Color-Texture Feature & Neural Network with Improved Computational Complexity using PCA
by Mohd Firoz Warsi, Ruqaiya Khanam, Usha Chauhan, Suraj Kamya
Abstract: The most severe kind of skin cancer is malignant melanoma. It can grow anywhere on the body. Its exact cause is still unclear but typically its caused by ultraviolet exposure from sun or tanning beds. Its detection plays a very significant role because if detected early then its curable, before the spread has begun. In this paper a computationally improved (using Principal Component Analysis, PCA) feature extraction method named 3D color texture feature (CTF) is represented which is well discriminative. For classification of melanoma from Dermoscopic images, a comparison of different types of machine-learning classification algorithms is evaluated, out of which back propagation neural network (NN) classifier outperforms all other and produce best results i.e. Accuracy = 98.5%, Sensitivity = 99.4%, Specificity = 95.0%. Obtained results are even better than benchmarking results of PH2 dataset. Comparisons of results with other similar novel works are also discussed.
Keywords: Melanoma; colour texture feature; Dermoscopic image; neural network classifier; PCA; PH2 and skin cancer.
Supervised Classification Approach for Cervical Cancer Detection using Pap Smear Images
by Pallavi Mulmule, Rajendra Kanphade
Abstract: Cervical Cancer is the found in women and is the global life threatening problem. Papanicolaou test is the well-known technique used for diagnosing the cancer at the early stage. However, the pathological screening is manual, tedious and time consuming process. Therefore, the proposed method employs adaptive fuzzy k means clustering to segment the cell containing nucleus and cytoplasm from the unwanted background from the pathological pap smear image. Thereafter, the 40 features are extracted from the segmented images based on the shape, size, intensity, orientation, color, energy and entropy of nucleus and cytoplasm individually. Finally, supervised classification approach utilizing multilayer perceptron with three kernel and support vector machine with five different kernels as the classifiers to predict the cancerous cells. The classifier is trained and tested on benchmark database with 280 pap smear images. The performance of these two classifiers are evaluated and found that the MLP classifier with hyperbolic tangent activation function outperforms in all the performance criterias as compared to SVM classifier with classification accuracy of 97.14%, sensitivity of 98%, Specificity of 95% and positive predictive value of 98%.
Keywords: Cervical cancer; Pap smear stain; pathological images; support vector machine; multi-layer perceptron; neural network.
MRI DENOISING: A SPARSE ICA BASED DICTIONARY LEARNING APPROACH
by Arathi Thiruvoth, Rahul C
Abstract: Image denoising is an important preprocessing technique in medical image analysis. The presence of noise in images can lead to degradation in its quality. Image denoising is the process of removing noise from an image and is basically a tradeoff between noise removal and preservation of significant image details. This paper presents a new sparse processing based denoising algorithm, the MMV-ICA (Multi-Median Variance-Independent Component Analysis) denoising algorithm. The MMV-ICA algorithm has been implemented and applied to medical images and the results are analyzed. Various noises which affect medical images are also considered. The proposed denoising algorithm is based on sparse and redundant representations over learned dictionaries. The dictionary is trained using the corrupted image. Thereafter, the dictionary is adapted to achieve sparse signal representations. MMV-ICA algorithm presented in this paper makes use of a patch based dictionary creation method. The paper presents the results of MMV-ICA denoising technique, which are found to be in par with the existing sparse based denoising methods.
Keywords: Sparse processing; Dictionary learning; Image Denoising; Independent Component Analysis (ICA.
Contact less non-invasive method to identify abnormal tongue area using k-mean and problem identification in COVID 19 scenario
by Pallavi Pahadiya, Ritu Vijay, Kumod Gupta, Shivani Saxena, Ritu Tandon
Abstract: Due to the spread of COVID-19 all around the world there is a need of automatic system for primary tongue ulcer, cancerous cell detection since, everyone dont go to hospital due to the panic and fear of virus spread. These diseases if avoided may spread soon. So, in such situation there is global need of improvement in disease sensing through remote devices using non-invasive methods. Automatic tongue analysis supports the examiner to identify the problem which can be finally verified using invasive methods. In automated tongue analysis image quality, segmentation and area of affected region plays an important role for disease identification. This paper proposes mobile based image sensing and sending the image to the examiner, examiner if finds issue in image may guide the user to go for further treatment. For Segmentation of abnormal area k-mean clustering is used with varying its parameters.
Keywords: Tongue diagnosis system (TDS); Image Acquisition; Thresholding; Segmentation; k-mean clustering; mobile app.
The efficacy of mechanical cervical traction for cervical spondylosis patients
by Hemlata Shakya, Shiru Sharma, Neeraj Sharma
Abstract: The aim of this study is to analyse the efficacy of cervical traction for spondylosis patients using wireless EMG sensor. Cervical spondylosis is a public health issue due to dizziness, headache, and neck pain. Based on the complaint and suggest the doctor's regularly for cervical traction treatment in the therapy unit. This study includes six cervical spondylosis patients for recording the EMG data using wireless EMG sensor in a sitting position. The subjects treated with 15 minutes of cervical traction with a 7 kg weight. The extracted various features in the time domain and frequency domain from the acquired EMG data to analysis the muscle fatigue during traction treatment. The pre-test evaluation showed that there are no significant differences (P > 0.05) and the post-test assessment showed a very high significance (P < 0.05) for outcome measurements. This statistical analysis showed that MAV, MF and MDF feature significant for spondylosis patients.
Keywords: cervical spondylosis; cervical traction; feature extraction.
Predicting anxiety disorders and suicide tendency using machine learning: a review
by Theodore Kotsilieris, Emmanuel Pintelas, Ioannis E. Livieris, Panagiotis Pintelas
Abstract: Anxiety disorders constitute the largest group and the most common type of mental disorders. At the same time, machine learning techniques can be used for analysing a patient's history and diagnose problems imitating the human reasoning or in making logical decisions. This work reviews the main concepts and applications of machine learning techniques in predicting anxiety disorder types. Seventeen (17) studies were considered, that applied machine learning techniques for predicting anxiety disorders and five (5) additional studies were examined for predicting suicide tendencies. The accuracy of the results varies according to the type of anxiety disorder and the type of methods utilised for predicting the disorder.
Keywords: machine learning; generalised anxiety disorder; panic disorder; agoraphobia; social anxiety disorder; posttraumatic stress disorder; suicide tendency.
Wavelet-based feature extraction technique for classification of different shoulder girdle motions for high-level upper limb amputees
by Ghaith K. Sharba, Mousa K. Wali, Ali H. Al-Timemy
Abstract: The aim of this study is to suggest a system for classification of seven classes of shoulder girdle motions for high-level upper limb amputees using pattern recognition (PR) system. In the suggested system, the wavelet transform was utilised for feature extraction and extreme learning machine (ELM) and linear discriminant analysis (LDA) were used as classifiers. The data were recorded from six intact-limbed subjects, and four amputees, with eight channels involving five electromyography (EMG) channels and 3-axis accelerometer. The study shows that the suggested pattern recognition system has the ability to classify the shoulder girdle motions with 92.67% classification accuracy for intact-limbed subjects and 87.67% classification accuracy for amputees by combining EMG and accelerometer channels. The outcomes of this study show that non-invasive PR system can help to provide control signals to drive a prosthetic arm for high level upper limb amputees.
Keywords: accelerometer; pattern recognition; ELM classifier; surface electromyography; upper limb amputation; wavelet transform; shoulder girdle.
Automated EEG-based epilepsy detection using BA_SVM classifiers
by Aya Naser, Manal Tantawi, Howida A. Shedeed, M.F. Tolba
Abstract: Epilepsy is a neurological disorder which affects individuals all around the world. The presence of epilepsy is recognised by seizures attacks. EEG signals can provide useful information about epileptic seizures. Unlike most of the existing studies which consider only two classes, this paper proposes an automatic EEG-based method for epilepsy detection which has the ability to distinguish between the three classes; normal, interictal (out of seizure time) and ictal (during seizure). In the proposed method, Rènyi entropy, line length and energy are computed from each of the five sub-bands extracted from an EEG segment using digital wavelet transform (DWT). Thereafter, the extracted features are fed into BA-SVM classifiers trained using divide and conquer strategy for classification. The BA-SVM classifier is a support vector machine (SVM) classifier whose parameters are optimised using BAT algorithm. The popular Andrzejak database was utilised for training and testing purposes. The average accuracies for all considered cases are more than or equal 95%. Thus, the various experiments and comparisons accomplished in this study reveal the efficacy of the proposed method.
Keywords: electroencephalogram; EEG; epilepsy; digital wavelet transform; DWT; entropies; support vector machine; SVM; bat optimisation.
Special Issue on: Health Engineering and Informatics
Bio-medical analysis of breast cancer risk detection based on deep neural network
by Nivaashini Mathappan, R.S. Soundariya, Aravindhraj Natarajan, Sathish Kumar Gopalan
Abstract: Breast tumour remains a most important reason of cancer fatality among women globally and most of them pass away due to delayed diagnosis. But premature recognition and anticipation can significantly diminish the chances of death. Risk detection of breast cancer is one of the major research areas in bioinformatics. Various experiments have been conceded to examine the fundamental grounds of breast tumour. Alternatively, it has already been verified that early diagnosis of tumour can give the longer survival chance to a patient. This paper aims at finding an efficient set of features for breast tumour prediction using deep learning approaches called restricted Boltzmann machine (RBM). The proposed framework diagnoses and analyses breast tumour patient's data with the help of deep neural network (DNN) classifier using the Wisconsin dataset of UCI machine learning repository and, thereafter assesses their performance in terms of measures like accuracy, precision, recall, F-measure, etc.
Keywords: breast tumour prediction; feature selection; restricted Boltzmann machine; RBM; deep neural network; DNN.
A review on emotion recognition in Parkinson's disease using bioinformatics
by K.N. Rejith, Kamalraj Subramaniam
Abstract: Parkinson's disease individuals have been stressed and shown difficulty in emotion recognition and facial expression with increasing cognitive decline. In recent years, various studies have been conducted in emotion recognition of Parkinson's disease patients. In many research works, emotional state assessment using facial expression and EEG-based stimuli were used for emotion recognition study. In this paper, a review of electroencephelogram (EEG)-based emotion recognition in Parkinson's disease and its various research analyses in the past two decades have been analysed. Most of the papers have investigated total of six emotions in Parkinson's disease study such as happiness, sadness, fear, anger, surprise, and disgust to evaluate the emotion difficulties in Parkinson's disease people.
Keywords: cognitive deficit; electroencephalogram; emotion; emotional deficits; event related potential; facial emotion recognition; nonlinear methods; Parkinson's disease.
A study on indirect immunofluorescence image classification methods for bioinformatics
by B.S. Divya, Kamalraj Subramaniam, H.R. Nanjundaswamy
Abstract: The indirect immunofluorescence (IIF) test with human epithelial type-2 (HEp-2) cells as substrates is the gold standard for anti-nuclear antibodies (ANA) test to diagnose autoimmune diseases. The specialists in the laboratory visually examine the specimen under microscope to recognise the staining patterns and generate the report. So ANA test is subjective and needs systemic automation for bioinformatics. In this view international benchmarking initiatives were organised by IAPR in the last six years. In this paper the state of the art on IIF HEp-2 cells classification task was analysed. This paper highlighted the original aspects with the detailed discussion of the published methods. Design choice verses performance was analysed.
Keywords: anti-nuclear antibodies; ANA; pattern classification; HEp-2 cell; computer aided diagnosis; CAD; indirect immunofluorescence; IIF; healthcare system.
Query optimisation with weighted fish school search in ontological database with application of bioinformatics
by R. Jaya, C.S. Pillai, R. Jagadeesh Kannan
Abstract: Making queries from large ontological database has a severe problem of generating query plans as it is made in form of left tree search form. This restricts the querying for composite applications and speed of acquiring query results. In such a scenario the most prominent approach is to optimise the indexing of graph nodes in ontological database and many evolutionary and particle of swarm optimisation (PSO)-based approach had already been attempted. However, loss of diversity and unanticipated convergence causes the solution to remain sub-optimal. In this study we present a weighted fish school searching-based query optimisation technique owing to its scalability and self control functioning with the application of bioinformatics. It creates probabilistic logic-based weight system for the fish school search in a hierarchical tree form which results in increased accuracy when put in comparison with standard PSO-based methods and its other variants.
Keywords: optimisation; semantic data; particle of swarm optimisation; PSO.
A cloud-based secured framework for smart medical diagnosis: a survey
by J. Leelavathy, S. Selva Brunda
Abstract: Enormous amounts of medical data are being collected by several well-developed hospital information systems (HIS) in the form of patient records in hospitals. The hidden patterns and relationships contained in this data are identified using various data mining techniques which have drawn increasing world-wide attention in the recent years. As a result of which, a good number of medical decision support systems (MDSS) have been developed. These are computer systems designed to assist physicians or other healthcare professionals in making clinical decisions for the given patient's symptoms and medical history. Inspired by the existing systems, the model proposed in this paper aims at making a knowledge sharing collaborative platform for doctors which serves as a realistic and effective medical decision support system. It is important because it provides vital information from different data sources. It has several challenges like scalability, response time, heterogeneous data formats. This paper does a detailed study of different challenges in understanding about medical information that can be provided to users in a better manner. A framework named intelligent healthcare framework (IHCF) is being proposed in this paper detailing the services and support to society.
Keywords: medical decision support system; MDSS; diagnosis; knowledge discovery; symptoms.