Transforming the Prediction of Heart Disease: An Empirical Analysis of Machine Learning Classifiers

The most prevalent diseases impacting the lives of individuals are those connected to the human body's most critical organ, the heart. It is crucial to emphasize that heart disease problems are not a new phenomenon and should not be sought after or welcomed as a trend, even if heart-related issues have received more attention and debate in recent years as a consequence of increased understanding and research. By leveraging their data processing and analytical skills, computers, Artificial Intelligence (AI) and the internet have revolutionized the identification and prediction of cardiac disease. The suggested model is evaluated and compared to current models that have been shown to be more accurate and effective, such as K-nearest Neighbor, Decision Tree, Logistic Regression, Random Forest and many other machines learning and deep learning Logistic Regression, Random Forest, K- nearest Neighbor and Decision Tree. After comparing and testing many models using various criteria, Random Forest revealed that the suggested model had the greatest accuracy 82.72%.