Coronary Artery Disease Diagnosis Using Supervised Fuzzy C-Means with Differential Search Algorithm-based Generalized Minkowski Metrics
Journal Title: Archive of Biomedical Science and Engineering - Year 2015, Vol 1, Issue 1
Abstract
Introduction: Coronary Artery Disease (CAD), one of the leading causes of death, is narrowing the walls of the coronary arteries. Angiography is the most accurate but invasive and costly CAD diagnosis method associated with mortality. The aim of this study was to design a computer-based non-invasive CAD diagnosis system. Methods: In this work, a dataset from Cleveland clinic foundation, containing 303 patients and 20 features, was used. Supervised Fuzzy C-means (SFCM) classification was used to design a classifier for CAD diagnosis. The Generalized Minkowski Metrics (GMM) was used to handle objects containing different measurement scale features. The performance of the SFCM was assessed with/without Statistical Feature Selection (SFS). The weights of the GMM, i.e. the significance of different features, beside other classifier parameters were tuned using Differential Search Algorithm (DSA), and the validity of the proposed classifier was further investigated. The hold-out and 10-fold cross validation were used for the performance assessment.
Authors and Affiliations
Negahbani Maryam, Joulazadeh Sanaz, Marateb Hamid Reza, Mansourian Marjan
Keywords
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- EP ID EP545496
- DOI 10.17352/abse.000002
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Coronary Artery Disease Diagnosis Using Supervised Fuzzy C-Means with Differential Search Algorithm-based Generalized Minkowski Metrics
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