Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease

• Published in: Computational and mathematical methods in medicine Vol. 2015; no. 2015; pp. 1 - 8
• Main Authors: Ghaemian, Ali, Bagheri, Somayeh, Abedi, Seyed Mohammad, Mohammadpour, Reza Ali
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2015
• Subjects: Adult; Aged; Computer Simulation; Coronary Angiography; Coronary Artery Disease - classification; Coronary Artery Disease - diagnosis; Cross-Sectional Studies; Female; Fuzzy Logic; Humans; Iran; Machine Learning; Male; Middle Aged; Models, Cardiovascular; Myocardial Perfusion Imaging; Iran
• ISSN: 1748-670X; 1748-6718; 1748-6718
• DOI: 10.1155/2015/564867

The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the characteristics, the results of myocardial perfusion scan (MPS), and coronary artery angiography of 115 patients, 62 (53.9%) males, in Mazandaran Heart Center in the north of Iran have been collected. We used membership functions for medical variables by reviewing the related literature. To improve the classification performance, we used Ishibuchi et al. and Nozaki et al. methods by adjusting the grade of certainty CFj of each rule. This system includes 144 rules and the antecedent part of all rules has more than one part. The coronary artery disease data used in this paper contained 115 samples. The data was classified into four classes, namely, classes 1 (normal), 2 (stenosis in one single vessel), 3 (stenosis in two vessels), and 4 (stenosis in three vessels) which had 39, 35, 17, and 24 subjects, respectively. The accuracy in the fuzzy classification based on if-then rule was 92.8 percent if classification result was considered based on rule selection by expert, while it was 91.9 when classification result was obtained according to the equation. To increase the classification rate, we deleted the extra rules to reduce the fuzzy rules after introducing the membership functions.