A Comparison of Supervised Machine Learning Algorithms on Heart Disease Risk Predictions

Cardiac disease detection and prediction have historically presented challenges for physicians, often requiring significant time and resources. Consequently, expensive therapies and operations are administered in hospitals and clinics to treat cardiac disorders. Therefore, early anticipation of cardiac disease holds immense potential in enabling individuals worldwide to seek timely treatment before the condition escalates. In recent years, heart disease has emerged as a prevalent global health issue, primarily attributed to excessive alcohol and tobacco use, as well as a lack of physical activity. This paper focuses on the utilization of machine learning methods to forecast cardiac illnesses. A comprehensive dataset encompassing diverse human health parameters is employed for training and testing purposes. Various AI and ML algorithms are implemented to predict cardiac disorders, and their performance is rigorously compared. The findings of this study contribute to the growing body of research on cardiac disease detection and prediction, highlighting the efficacy of machine learning approaches. By leveraging these algorithms, healthcare professionals can potentially identify individuals at risk of cardiac disease at an early stage, enabling proactive intervention and preventive measures. Moreover, the comparative analysis of multiple machine learning algorithms offers valuable insights into their respective strengths and limitations, aiding in the selection of the most suitable approach for specific clinical scenarios.