An ensemble-driven machine learning framework for robust heart disease prediction using dimensionality reduction and comparative evaluation 

Coronary heart disease is a major cardiovascular condition characterized by reduced coronary blood flow, often due to atherosclerotic narrowing of the coronary arteries. Early and prompt identification of these abnormalities is critical to minimizing healthcare costs and improving patient survival rates. Despite impressive progress in predictive modeling, many existing machine learning models suffer from overfitting, in which they perform well on training or test data but fail to generalize to larger and more heterogeneous patient populations. This limitation diminishes their clinical validity and applicability in medical diagnosis. To address these issues, the proposed study presents an ensemble-based machine learning approach for the early prediction of heart disease. The proposed system incorporates several supervised learning algorithms to increase predictive accuracy and generalization performance. Decision tree, support vector machine, random forest, and classifiers trained on principal component analysis-transformed features were compared. The framework involves systematic data preprocessing, dimensionality reduction, and class imbalance management to ensure methodological robustness and consistency of diagnostic outcomes. Experimental results demonstrate that the ensemble-based approach achieves superior predictive performance while minimizing data bias. These findings highlight the potential of machine learning-based diagnostic systems to support clinical decision-making, improve healthcare resource allocation, and enhance patient outcomes by enabling timely and accurate cardiovascular risk identification.