Optimized feature selection and advanced machine learning for stroke risk prediction in revascularized coronary artery disease patients.

Journal: BMC medical informatics and decision making
Published Date: Jul 24, 2025

Abstract

BACKGROUND: Coronary artery disease (CAD) remains a leading cause of global mortality, with stroke constituting a significant complication among patients undergoing coronary revascularization procedures, such as percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). Previous research has demonstrated the successful application of machine learning (ML) in predicting various postoperative outcomes, including poor prognosis following cardiac surgery and the risk of postoperative stroke. Despite these advancements, a critical gap persists in studies quantitatively linking the risk of postoperative stroke to revascularization using ML-based approaches. This study aims to address this gap by developing and validating ML models to predict the risk of stroke in CAD patients undergoing coronary revascularization, with the ultimate goal of enhancing clinical decision-making and improving patient outcomes.

Authors

  • Yong Si
    University of Southern California, Los Angeles, CA, USA.
  • Armin Abdollahi
    Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
  • Negin Ashrafi
    Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles, CA, United States of America.
  • Greg Placencia
    California State Polytechnic University, Pomona, CA, USA.
  • Elham Pishgar
    Colorectal Research Center, Iran University of Medical Sciences, Iran.
  • Kamiar Alaei
    Department of Health Science, California State University, Long Beach, 1250 Bellflower Blvd. HHS2-117, Long Beach, CA, 90840, USA.
  • Maryam Pishgar
    Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60609, USA.

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