Design and Development of an Efficient Explainable AI Framework for Heart Disease Prediction

Abstract

Heart disease remains a global health concern, demanding early and accurate prediction for improved patient outcomes. Machine learning offers promising tools, but existing methods face accuracy, class imbalance, and overfitting issues. In this work, we propose an efficient Explainable Recursive Feature Elimination with eXtreme Gradient Boosting (ERFEX) Framework for heart disease prediction. ERFEX leverages Explainable AI techniques to identify crucial features while addressing class imbalance issues. We implemented various machine learning algorithms within the ERFEX framework, utilizing Support Vector Machine-based Synthetic Minority Over-sampling Technique (SVMSMOTE) and SHapley Additive exPlanations (SHAP) for imbalanced class handling and feature selection with explainability. Among these models, Random Forest and XGBoost classifiers within the ERFEX framework achieved 100% training accuracy and 98.23% testing accuracy. Furthermore, SHAP analysis provided interpretable insights into feature importance, improving model trustworthiness. Thus, the findings of this work demonstrate the potential of ERFEX for accurate and explainable heart disease prediction, paving the way for improved clinical decision-making.