Cardiovascular Predictive Analysis with Machine Learning Fusion

Predicting heart strokes is a critical task in the field of healthcare, and it is further complicated by the multifaceted nature of cardiovascular health. In recent years, machine learning algorithms have emerged as valuable tools for forecasting heart stroke occurrences. Among these methods, ensemble models have shown significant promise by amalgamating the predictions of multiple base models. To overcome the existing limitations and enhance the accuracy of heart stroke prediction, the research introduces a novel ensemble model, combining stacked long short-term memory (LSTM) and XGBoost, which leverages the strengths of both these algorithms. The proposed model is compared with alternative methods, including individual stacked LSTM and XGBoost models, to assess its effectiveness in predicting heart strokes. The ensemble model is trained and evaluated using extensive historical health data. Evaluation metrics such as accuracy, precision, F1 score, and recall are employed to assess the performance of the forecasting model. The results of this study reveal that the ensemble stacked LSTM-XGBoost model outperforms individual stacked LSTM and XGBoost models, demonstrating superior predictive accuracy in capturing heart stroke occurrences.