• Shikha Sharma
• Prof.Brajesh Patel

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, necessitating accurate and interpretable prediction systems to support clinical decision-making. Although machine learning (ML) techniques have demonstrated significant potential in heart disease prediction, many existing models operate as black boxes, limiting their adoption in healthcare environments. This paper presents a hybrid Machine Learning and Explainable Artificial Intelligence (XAI) framework for cardiovascular disease prediction using structured healthcare data. The proposed approach integrates Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) classifiers with ensemble learning techniques, including voting and stacking. To enhance transparency and trustworthiness, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) are employed to provide global and local explanations of model predictions. Experiments conducted on the UCI Heart Disease dataset demonstrate that the stacking ensemble achieves superior predictive performance, obtaining an accuracy of 96.0%, precision of 95.5%, recall of 96.4%, F1-score of 95.9%, and ROC-AUC of 0.98. Interpretability analysis identifies chest pain type, maximum heart rate achieved, ST depression, and number of major vessels as the most influential risk factors. The results indicate that the proposed hybrid ML-XAI framework effectively combines high predictive accuracy with model transparency, making it a promising solution for intelligent clinical decision support systems.

Cardiovascular Disease Prediction, Machine Learning, Ensemble Learning, Explainable Artificial Intelligence, SHAP, LIME, Clinical Decision Support Systems.

"A Hybrid Machine Learning and Explainable AI Framework for Cardiovascular Disease Prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 9, page no.b143-b157, September-2026, Available :http://www.jetir.org/papers/JETIR2609116.pdf

"A Hybrid Machine Learning and Explainable AI Framework for Cardiovascular Disease Prediction ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 9, page no. ppb143-b157, September-2026, Available at : http://www.jetir.org/papers/JETIR2609116.pdf