Heart Attack Prediction in The United States: A Review - Mustafa , Botan Shivan; Kareem , Omar Sedqi (International Journal of Scientific World, 2025)
Cardiovascular disease remains a predominant health concern in the United States, with over 600,000 annual fatalities, underscoring the necessity for advanced predictive methodologies. This review examines the integration of artificial intelligence (AI) and machine learning (ML) techniques in forecasting myocardial infarctions through the analysis of clinical, behavioral, and demographic data. Various supervised learning algorithms, including Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting, Logistic Regression, and Deep Neural Networks (DNN), have demonstrated significant diagnostic accuracy. The application of optimization strategies such as Particle Swarm Optimization (PSO), dimensionality reduction methods like Principal Component Analysis (PCA), and automated machine learning (AutoML) frameworks has enhanced model efficiency and adaptability. Nonetheless, challenges persist, notably in real-time deployment, dataset representativeness of minority populations, interpretability of complex models, and cross-environment generalizability. This synthesis highlights current advancements, identifies key limitations, and suggests future research directions focused on developing scalable, interpretable, and equitable predictive systems to facilitate early detection and personalized cardiac care.