International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Heart attack prediction remains a critical component of preventive cardiology, requiring highly accurate and interpretable machine learning (ML) frameworks capable of identifying high-risk individuals before the onset of acute myocardial events. Traditional approaches often suffer from limited diagnostic precision due to noisy clinical attributes, heterogeneous patient data, and the absence of systematic feature-engineering strategies. Recent advancements in ensemble machine learning have demonstrated significant improvements in risk-stratification performance by combining multiple weak or strong learners and extracting the most influential clinical predictors. This study reviews and synthesizes recent developments in heart attack prediction models, focusing on ensemble-based architectures, feature-selection techniques, and hybrid frameworks that integrate clinical, demographic, and biochemical features.

Special attention is given to the base methodology, which utilizes Random Forest (RF), stacking, and SelectKBest feature engineering to achieve superior precision, recall, and F1-score compared to contemporary works. While numerous ML models exhibit strong performance in cardiovascular prediction tasks, many report lower accuracy than the ensemble-driven framework presented in the base study, primarily due to dataset imbalance, limited feature optimization, and suboptimal model generalization. Through a comparative analysis of eighteen related research publications, this literature survey highlights the strengths, limitations, and methodological gaps across current heart attack prediction studies, ultimately reinforcing the effectiveness of ensemble ML coupled with robust feature engineering as a powerful strategy for early heart attack risk assessment.

Keywords: Ensemble Machine Learning, Feature Engineering, SelectKBest, Random Forest, Stacking Classifier, Clinical Risk Factors, Cardiovascular Disease Detection, Predictive Modelling, Medical Data Analysis.

Downloads: | DOI: 10.17148/IJARCCE.2025.141221