Heart Attack Risk Prediction Using Advanced Machine Learning Techniques

Addressing the urgent global health challenge of cardiovascular diseases, particularly heart attacks, this research paper explores proactive healthcare solutions driven by effective predictive strategies. Machine learning and data mining methodologies play a pivotal role in navigating the dynamic landscape of heart attack risk prediction. Emphasizing accuracy and personalization, we conduct a meticulous examination of innovative datasets and employ advanced algorithms. Our comprehensive evaluation spans four distinct machine learning approaches-Random Forest, Support Vector Machine (SVM), XGBoost, and the Stacking Classifier ensemble, specifically incorporating Logistic Regression, Decision Tree, and k-Nearest Neighbors as base classifiers. This strategic combination captures their individual strengths to optimize predictive performance and create a more robust final model, providing a nuanced comparison of their predictive capabilities.Utilizing four datasets, our study reports the highest accuracy achieved by each algorithm: XGBoost at 97.5at 97insights to the ongoing discourse, fostering the refinement of predictive models and propelling forward preventive healthcare strategies for enhanced patient outcomes within the complex landscape of cardiovascular challenges.