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Improving Coronary Artery Disease Prediction: Use of Random Forest, Feature Importance and Case-Based Reasoning

Author

Listed:
  • Fouad Henni

    (CSTL Lab. Mostaganem, Mostaganem University, Algeria)

  • Baghdad Atmani

    (LIO Lab. Oran, Mostaganem University, Algeria)

  • Fatiha Atmani

    (Mostaganem University, Algeria)

  • Fatima Saadi

    (LIO Lab. Oran, Oran 1 University, Algeria)

Abstract

Cardiovascular diseases (CVDs) are the number one cause of death globally. Coronary artery disease (CAD) is the most common form of CVDs. Abundant research works propose decision support systems for CAD early detection. Most of proposed solutions have their origins in the realm of machine learning and datamining. This paper presents two solutions for CAD prediction. The first solution optimizes a random forest model (RFM) through hyperparameters tuning. The second solution uses a case-based reasoning (CBR) methodology. The CBR solution takes advantage of feature importance to improve the execution time of the retrieve step in the CBR cycle. The experimentations show that the RFM outperformed most recent published models for CAD diagnosis. By reducing the number of attributes, the CBR solution improves the execution time and also performs very well in terms of diagnosis accuracy. The performance of the CBR solution is intended to be enhanced because CBR is a learning methodology.

Suggested Citation

  • Fouad Henni & Baghdad Atmani & Fatiha Atmani & Fatima Saadi, 2023. "Improving Coronary Artery Disease Prediction: Use of Random Forest, Feature Importance and Case-Based Reasoning," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 15(1), pages 1-17, January.
    • Handle: RePEc:igg:jdsst0:v:15:y:2023:i:1:p:1-17
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