Comparative analysis of machine learning models for coronary artery disease prediction with optimized feature selection.

Journal: International journal of cardiology
Published Date: May 31, 2025

Abstract

BACKGROUND: Coronary artery disease (CAD) is a major global cause of death, necessitating early, accurate prediction for better management. Traditional diagnostics are often invasive, costly, and less accessible. Machine learning (ML) offers a non-invasive alternative, but high-dimensional data and redundancy can hinder performance. This study integrates Bald Eagle Search Optimization (BESO) for feature selection to improve CAD classification using multiple ML models.

Authors

  • David B Olawade
    Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom.
  • Afeez A Soladoye
    Department of Computer Engineering, Federal University, Oye, Ekiti, Nigeria.
  • Bolaji A Omodunbi
    Department of Computer Engineering, Federal University, Oye, Ekiti, Nigeria.
  • Nicholas Aderinto
    Department of Medicine and Surgery, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
  • Ibrahim A Adeyanju
    Department of Computer Engineering, Federal University, Oye, Ekiti, Nigeria.

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