International Journal of Functional Informatics and Personalised Medicine
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International Journal of Functional Informatics and Personalised Medicine (3 papers in press)
An Overview of Real-world Clinical Data Mining on TCM by Xiaoping Zhang Abstract: This paper provides a survey of commonly used data mining methods that have been applied to real-world TCM clinical data in recent years, and sets forth the requirements of data mining on real-world TCM clinical data, in order to provide reference for better analyzing the syndrome differentiation and treatment principle hidden in the massive TCM clinical data in the future. Keywords: Real-world; data mining; Traditional Chinese medicine (TCM); clinical diagnosis and treatment.
Predicting and Analyzing Breast Cancer tumors by adopting Hybrid Neuro Fuzzy approach by Sanjiban Sekhar Roy, Saptarshi Chakraboty, P.V. Krishna, Ajith Abraham Abstract: It is often observed and criticized that health experts demonstrate variability in treatment while making crucial decisions regarding the diagnosis of a patient. These variations of decision making not only depends upon the condition of the patient; but also the doctors experience in that particular field plays a huge part in such crucial decision making. To solve these types of problem, researchers and medical experts have suggested setting a standard set of parameters to analyze the different conditions. Automated (computerized) models were also proposed as these models dont exhibit this variability while making crucial decisions. The benefit of computerized model is its consistency and accuracy in decision making. In this paper, we have proposed a hybrid model to simulate a breast cancer treatment scenario based on the medical history of the patients stored in the hospital database. Our proposed approach can detect whether the tumor is benign or malignant based on the assessment of nine different biological attributes. We have tested the performance of our proposed model on the Wisconsin Breast Cancer Database and compared the result obtained from our proposed approach with the other existing machine learning techniques. Through different experiments, we have established the effectiveness of our proposed algorithm. Keywords: Neuro-Fuzzy; Breast Cancer; Hybrid Method; ANFIS.
A demonstration of the effects of the number of fuzzy states on a control system by Maged N. Kamel Boulos Abstract: In this paper, we describe a fuzzy logic demonstrator that uses heart rate and skin impedance values as inputs to predict a hypoglycaemia index. The demonstrator was created using JFS v2.00 fuzzy logic freeware and is freely available on the authors Web site at http://healthcybermap.org/FL/index.htm. We investigate the effects of varying the number of fuzzy states (adjectives or membership functions) in our demonstrator. Iterative experimentation and tuning are always necessary to determine the ideal number and configuration of fuzzy sets for solving a particular clinical problem. The exercise presented in this article is meant for educational purposes, to provide new students of this important subject with some fundamental insights into the inner working of fuzzy logic systems in their quest to simulate the decision-making process of a human expert. Keywords: Fuzzy logic; Fuzzy states; Control systems; Clinical decision support systems; Hypoglycaemia; Diabetes mellitus.