Ensemble Learning for Coronary Heart Disease Detection: A Machine Learning Approach

Abstract

Coronary Heart Disease (CHD) has remained one of the foremost causes of death in the world, and thus, there is a need to ensure that there are dependable early diagnosis mechanisms that would aid clinicians in making decisions at the right time. The rapid development of electronic health records and sensor-based medical data has presented more opportunities in predictive analytics in healthcare than ever before. However, the sensitivity, complexity, and scale of health data require robust analytical models and a safe and reliable data processing system. In this respect, machine learning (ML) methods have become effective instruments in deriving significant patterns of heterogeneous healthcare data. This study hypothesizes an ensemble learning framework that is used in the early identification of CHD. The proposed ensemble model is more accurate and stronger in predictions than any of the individual models by incorporating several ML classifiers. The study provides a scalable method to prevent cardiovascular diseases, and the model may help healthcare professionals to identify high-risk patients at an early stage and, thus, implement interventions in time and enhance patient outcomes. Experimental results demonstrate that the ensemble model outperforms conventional ML models, highlighting its effectiveness as a supportive diagnostic tool for CHD prediction.