Benchmarking of feature selection techniques for coronary artery disease diagnosis

Abstract

Coronary artery disease (CAD) is a disease that causes many deaths in human. CAD occurs when the atherosclerosis (fatty deposits) blocks blood flow to the heart muscle in the coronary arteries. The gold standard method to diagnose CAD is coronary angiography. However, this method is invasive, risky and costly. Therefore, it is necessary to develop a method for diagnosing the CAD before coronary angiography is performed. The objective of this research is to provide a benchmark comparison of the feature selection techniques in the diagnosis of CAD. A total of four feature selection methods are used. These methods are motivated feature selection (MFS), correlation based feature selection (CFS), wrapper based feature selection (WFS) and rough set based feature selection (RST). The Naïve Bayes and J48 classifiers are used to diagnose the presence of CAD. The result shows that WFS and CFS are superior compared to MFS and RST.