Harmonizing Ensemble Learning Strategies for Enhanced Coronary Artery Disease Prediction

Coronary artery disease (CAD) is a common health issue today that can lead to financial loss, disability, and even death for those with heart problems. In addition to increasing the chances of curing patients, early diagnosis could lead to a reduction in their impermanence as well. Machine learning algorithms are now often employed in medicine and have proven to be reliable, saving both time and lives. In order to enhance precision, Machine Learning models are trained using diverse datasets to attain more accurate results. In this research, our goal is to improve the accuracy of predicting heart disease by training different machine learning models with the aid of these datasets. Rather than having costly medical examinations, we want to cut down on time and money spent. In this way, patients will not have to wait long for multiple tests of this type. This paper demonstrates how advanced machine learning algorithms can help predict CVD using simple medical tests.