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Updated: May 26, 2026

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Leveraging Clinical Data for Early Heart Disease Prediction: A Machine Learning Approach With Interpretability

Emma Qumsiyeh1, Qassam Al-Wirdian1, Nur Sebnem Ersoz2

  • 1Faculty of Engineering and Information Technology, Palestine Ahliya University, Bethlehem, Palestine.

Biomedical Engineering and Computational Biology
|May 19, 2026
PubMed
Summary

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Machine learning models, particularly Random Forest and K-Nearest Neighbors (KNN), show strong potential for accurate heart disease prediction. Explainable AI (SHAP) enhances model interpretability for clinical decision support.

Area of Science:

  • Cardiology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Heart disease is a major global cause of mortality.
  • Early and accurate diagnosis is crucial for effective prevention and treatment.
  • This necessitates advanced diagnostic tools for risk stratification.

Purpose of the Study:

  • To develop and evaluate machine learning models for heart disease prediction.
  • To compare the performance of Logistic Regression, Random Forest, KNN, and Decision Trees.
  • To enhance model interpretability using SHAP values for clinical trust.

Main Methods:

  • Utilized a publicly available clinical and demographic dataset.
  • Performed data preprocessing including imputation, encoding, and normalization.
Keywords:
SHAP explainabilityclinical decision supportdata preprocessingearly diagnosisheart diseasemachine learningpredictive modeling

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  • Evaluated four classification algorithms (Logistic Regression, Random Forest, KNN, Decision Trees) using accuracy, precision, recall, and AUC-ROC metrics.
  • Applied SHapley Additive exPlanations (SHAP) for model interpretability.

    • Main Results:

    • Hyperparameter-optimized Random Forest and KNN models demonstrated superior predictive performance.
    • SHAP analysis provided insights into feature importance and individual prediction explanations.
    • The study confirmed the effectiveness of machine learning in predicting heart disease.

    Conclusions:

    • Interpretable machine learning models offer significant potential for early heart disease diagnosis and clinical decision support.
    • SHAP enhances transparency and clinical trust in AI-driven diagnostic tools.
    • Future work should focus on larger datasets and real-time applications to improve generalizability and clinical utility.