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

Authors: Bikesh Kumar Singh; Sonali Dutta; Poonam Chand; Khilesh Kumar; Sumit Kumar Banchhor

Addresses: Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India ' Department of Electronics and Telecommunication, RSR – Rungta College of Engineering and Technology, Bhilai, Chhattisgarh, India ' Department of Electronics and Telecommunication, RSR – Rungta College of Engineering and Technology, Bhilai, Chhattisgarh, India ' Department of Electronics and Telecommunication, RSR – Rungta College of Engineering and Technology, Bhilai, Chhattisgarh, India ' Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, India

Abstract: 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.

Keywords: coronary artery disease; CAD; multi-criteria feature selection; machine learning; classification; support vector machine; SVM; kernel functions; cross-validation; accurate; automated; reliable.

DOI: 10.1504/IJCVR.2023.133146

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.5, pp.533 - 555

Received: 22 Jan 2021
Accepted: 18 May 2022
Published online: 01 Sep 2023 *

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