International Journal of Electronic Healthcare (4 papers in press)

Regular Issues

• Development of Usability Features for Mobile Nutrition  
  by Raquel Troccola, June Wei 
  Abstract: Development of mobile nutrition (m-nutrition) health applications is evolving rapidly due to technology. From a usability perspective, user-friendly features can successfully help nutrition communication systems. The current paper first develops a framework model to study usability features from food diary/weight management nutrition applications. Then, identifies usability features based on telehealth communications to help people motivate and reach nutrition and exercise goals. This might include tracking food intake, educating consumers about food, and starting an exercise regime. Then, ten popular nutrition applications were identified to empirically analyse more in-depth their features by conducting cluster analysis. User-friendly and visually appealing functions are important to attract consumers. This research will be beneficial to registered dietitian nutritionists, nutrition companies and smartphone application developers in the health and fitness area.
  Keywords: telehealth; smartphone applications; m-health; mobile nutrition systems; health; diet tracker; food diary; weight management; nutrient intake.
  DOI: 10.1504/IJEH.2023.10055034
   
  
• Diagnosing Obesity using Classification based Machine Learning Models  
  by Udeechee Udeechee, VIJAY KUMAR T.V., Aayush Goel 
  Abstract: Obesity has been a major underlying risk for people with chronic diseases and, thus, needs to be diagnosed in the early stages. This requires clinical data related to potential obese individuals to be evaluated for diagnosing obesity levels in individuals. In this paper, classification based machine learning techniques have been applied on such clinical data to design obesity classification models, which would be capable of diagnosing whether an individual is obese or not. Classification techniques, including ensemble techniques, were used to design such obesity classification models. The performance of these obesity classification models was evaluated and compared on metrics such as accuracy, precision, recall, F1-score and area under the receiver operating characteristic curve. Experimental results showed that the use of ensemble techniques improved the performance of the obesity classification models. Further, amongst the ensemble techniques, the boosting technique based obesity classification model performed the best.
  Keywords: healthcare; disease; obesity; artificial intelligence; AI; machine learning; ML; classification techniques.
  DOI: 10.1504/IJEH.2023.10055923
   
  
• A Cough Type Chronic Disease Prediction Scheme Using Machine Learning and Diagnosis Support System Using a Mobile Application  
  by Upol Chowdhury, M. Hoq Chowdhury 
  Abstract: Machine learning has been found to considerably lower the probability of inaccurate diagnoses when incorporated into modern diagnostic procedures. Different from the literature works, this paper proposes a method for diagnosing the three most similarly symptomised cough-type chronic diseases: COPD, bronchial asthma, and pneumonia. The symptoms of cough-type chronic disease patients admitted to the hospital were collected from eight medical colleges spread out over Bangladesh to construct the classifying model. This paper proposes a set of 27 attributes for appropriately classifying cough-type chronic disease instances. Several machine learning methods are tested using the dataset. Our research suggests gradient tree boosting to be the most effective, with a classification accuracy of 91%, despite the fact that previous research has identified support vector machine and random forest to be the most efficient models in these kinds of classification tasks. This paper also developed a mobile application for the diagnostic support systems.
  Keywords: cough type chronic disease; machine learning; prediction; evaluation; healthcare; mobile application.
  DOI: 10.1504/IJEH.2023.10059444
   
  
• Diabetes Detection System (DDS) Using Machine Learning Algorithms  
  by Salliah Bhat Shafi, Madhina Banu, Gufran Ansari, Venkatesan Selvam 
  Abstract: Diabetes is a major severe disease that affects a lot of people worldwide. Technical advances have rapid impact on many aspects of human life whether it is healthcare profession or any other field. The disorder has an impact on society. Machine learning algorithms (MLA) can aid in predicting the chance of developing diabetes at a young age, assist in improving diabetes clinical condition. The proposed framework can be used in healthcare industry for prediction of diabetes detection and prediction in North Kashmir. Four MLA have been successfully used in the experimental study such as random forest, K-nearest neighbour, support vector machine and naive Bayes. KNN is most accurate classifier with highest accuracy rate of 97.29% accuracy rate in comparison to other methods with the balanced dataset. Overall, this study enables us to effectively identify the prevalence and prediction of diabetes.
  Keywords: diabetes; machine learning algorithms; MLA; framework; random forest; K-nearest neighbour; KNN; support vector machine; SVM.
  DOI: 10.1504/IJEH.2023.10060705