ArticleComputational and mathematical methods in medicine2015
Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications.Sensors (Basel, Switzerland) · 2023Article
- Predictive Sequential Research Design to Study Complex Social Phenomena.Entropy (Basel, Switzerland) · 2021Article
- Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems.Diagnostics (Basel, Switzerland) · 2019Article
- Automated Diagnosis of Coronary Artery Disease: A Review and Workflow.Cardiology research and practice · 2018Review
- Machine Learning Approaches in Cardiovascular Imaging.Circulation. Cardiovascular imaging · 2017Review
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Authors and funding
4 authors.
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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 CF j 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.
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