Innovative XAI Approaches for Predicting Cardiovascular Diseases

Understanding why a machine learning model makes a certain prediction is just as critical as how accurately it predicts, especially when it comes to diagnosing and treating cardiovascular disease. In this study, we applied explainable artificial intelligence (XAI) techniques to improve both the predictive power and interpretability of heart disease detection models. A dataset of 1,025 patient records was thoroughly preprocessed, including the handling of missing values, the encoding of categorical features, and the binarization of the outcome variable. We evaluated several machine learning models LSTM networks, Random Forest, Gradient Boosting, XGBoost, and Logistic Regression using performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. While XGBoost emerged as the most accurate model, we moved beyond accuracy to explore the root causes of such strong performance. To accomplish this, we employed three key XAI techniques: SHAP (SHapley Additive exPlanations) to quantify how individual features influenced predictions, LIME (Local Interpretable Model-Agnostic Explanations) to provide intuitive, local-level explanations for individual predictions, and Permutation Feature Importance to assess which features most affected model performance when altered. These methods transformed XGBoost from a high-performing black-box into a transparent and trustworthy diagnostic tool. Our findings underscore how integrating explainability into AI pipelines leads to not just accurate predictions, but also clinically actionable insights highlighting the most important risk factors for cardiovascular disease and supporting more informed, responsible medical decisions.