Fuzzy Rule-Based Classification System for Assessing Coronary Artery DiseaseReport as inadecuate




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Computational and Mathematical Methods in Medicine - Volume 2015 2015, Article ID 564867, 8 pages -

Research Article

Department of Biostatistics, Faculty of Health, Diabetes Research Center, Mazandaran University of Medical Sciences, Sari 4817844718, Iran

Department of Radiology, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari 4817844718, Iran

Department of Biostatistics, Faculty of Health, Zabol University of Medical Sciences, Zabol, Iran

Mazandaran Heart Center, Mazandaran University of Medical Sciences, Sari 4817844718, Iran

Received 17 April 2015; Revised 14 July 2015; Accepted 21 July 2015

Academic Editor: Chuangyin Dang

Copyright © 2015 Reza Ali Mohammadpour et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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 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.





Author: Reza Ali Mohammadpour, Seyed Mohammad Abedi, Somayeh Bagheri, and Ali Ghaemian

Source: https://www.hindawi.com/



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