An improved multi-criteria-based feature selection approach for detection of coronary artery disease in machine learning paradigm.

This paper presents an accurate approach for the detection of coronary artery disease (CAD) using an improved multi-criteria feature selection (IMCFS) approach in a machine learning (ML)-based paradigm. This study uses the Z-Alizadeh Sani dataset of CAD, consisting of 303 patients with 56 different attributes. The proposed IMCFS-based approach uses seven different traditional feature selection techniques. For classification, the support vector machine is used with four different kernel functions and is evaluated using three cross-validation protocols. Lastly, performance is evaluated using five measures. The proposed IMCFS-based approach using the 30 most relevant features outperforms all other traditional feature selection techniques and achieved the highest classification accuracy, sensitivity, specificity, the area under receiver operating characteristics, and Mathew's correlation coefficient of 91.9%, 95.7%, 82.1%, 88.9% and 79.7%, respectively. The proposed IMCFS-based approach is an entirely reliable, automated, and highly accurate ML tool for detecting CAD.