An Explainable Artificial Intelligence (XAI) Methodology for Heart Disease Classification

Heart disease continues to be one of the predominant contributors to morbidity and mortality on a global scale, underscoring the imperative for early and precise diagnosis to enhance patient outcomes. Machine Learning (ML) has emerged as a formidable instrument in the classification of cardiovascular diseases, utilizing intricate clinical datasets to discern patterns that conventional statistical methodologies may fail to detect. Nevertheless, notwithstanding their robust predictive capabilities, numerous machine learning models function as black-box systems, exhibiting a deficiency in transparency regarding their decision-making processes. The absence of interpretability presents a considerable challenge in clinical environments, where trust, accountability, and elucidation are of utmost importance for medical professionals. In order to tackle this issue, we propose a methodology for heart disease classification that is grounded in Explainable Artificial Intelligence (XAI). This approach incorporates interpretable machine learning models to improve diagnostic transparency and reliability. Our framework conducts an evaluation of various classifiers, including Support Vector Machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and LightGBM. This assessment is based on essential performance metrics, namely accuracy, precision, recall, F1-score, and AUC-ROC. Furthermore, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) have been integrated to enhance the interpretability of the model. The experimental findings indicate that XGBoost surpasses alternative models, attaining the highest classification accuracy of 92% and an AUC-ROC score of 0.93, all while preserving interpretability. This study underscores the significance of incorporating Explainable Artificial Intelligence (XAI) techniques within medical AI applications. It advocates for the adoption of transparent, interpretable, and clinically dependable machine learning methodologies to enhance clinical decision-making and optimize patient outcomes.