XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach

Abstract

Ensemble Learning (EL) has been used for almost ten years to classify heart diseases, but it is still difficult to grasp how the "black boxes", or non-interpretable models, behave inside. Predicting heart disease is crucial to healthcare, since it allows for prompt diagnosis and treatment of the patient's true state. Nonetheless, it is still difficult to forecast illness with any degree of accuracy. In this study, we have suggested a framework for the prediction of heart disease based on Explainable artificial intelligence (XAI)-based hybrid Ensemble Learning (EL) models, such as LightBoost and XGBoost algorithms. The main goals are to build predictive models and apply SHAP (SHapley Additive expPlanations) and LIME (Local Interpretable Model-agnostic Explanations) analysis to improve the interpretability of the models. We carefully construct our systems and test different hybrid ensemble learning algorithms to determine which model is best for heart disease prediction (HDP). The approach promotes interpretability and transparency when examining these widespread health issues. By combining hybrid Ensemble learning models with XAI, the important factors and risk signals that underpin the co-occurrence of heart disease are made visible. The accuracy, precision, and recall of such models were used to evaluate their efficacy. This study highlights how crucial it is for healthcare models to be transparent and recommends the inclusion of XAI to improve interpretability and medical decisionmaking.

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