Explainable Ensemble Machine Learning Framework For Heart Disease Prediction

• Author(s):

  Mr. P. Ranjith | Krithik Ragul S | Moses Praveen R | Vigneshwaran M

• Keywords:

  Heart Disease Prediction, Machine Learning, Random Forest, XGBoost, Logistic Regression, Explainable AI, SHAP, LIME, Healthcare Prediction.

• Abstract:

  Heart Disease Is One Of The Leading Causes Of Death Worldwide, And Early Prediction Plays An Important Role In Reducing Severe Health Risks. Traditional Diagnostic Methods Such As ECG, Blood Tests, And Imaging Require Expert Interpretation, More Time, And Higher Cost. This Paper Proposes An Efficient Heart Disease Prediction System Using Machine Learning Techniques To Predict The Possibility Of Heart Disease Based On Clinical Data Such As Age, Blood Pressure, Cholesterol, Chest Pain Type, ECG Results, And Heart Rate. The System Applies Algorithms Such As Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, And XGBoost. Among These Models, Random Forest And XGBoost Provide Better Accuracy And Reliability. Explainable AI Techniques Such As SHAP And LIME Are Also Used To Make The Prediction Results Transparent And Understandable For Doctors And Patients.

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Other Details

• Paper id:

  IJSARTV12I5105412

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-20

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