Revolutionizing Cardiovascular Disease Prediction: Stacked Ensemble Models with XGBoost, Neural Networks, and Generative AI Integration

cardiovascular disease hence makes a sizable portion of global mortality, further calling for ideal and timely prediction measures. The comparison of four machine learning models, namely, Logistic Regression, Random Forest, XGBoost, and Neural Networks for cardiovascular disease prediction is done in this work. Therefore, to improve the prediction accuracy still further, the present study advocated the use of a new Stacking Model as the augmented learner through Random Forest and XGBoost algorithms. When compared with individual models, the accuracy of the Stacking Model is extraordinary is 90.00% higher than other baselines including Logistic Regression (72.36%), Random Forest (71.41%), XGBoost (73.57%), and Neural Networks (73.18). Within the system setting, there is a Generative AI chatbot where users can ask questions about the predictions and factors behind them. Moreover, a user-oriented feature implemented via an SMTP based Email Notification Service means timely alerting of severe predictions and/or changes. The combination of machine learning, Generative AI, and email notification shows how more prompt and efficient, easy to use diagnostic tools can be created for the future with appeal to personalized health solutions.