Supervised Machine Learning in Cardiology: A Predictive Model for Heart Disease

One of the worst diseases in the world, heart disease takes a great toll on lives every year. In order to treat this potentially fatal illness, a tool or gadget must be practical, accurate, and dependable. This will allow for prompt detection or prediction, which will lead to efficient treatment and a decline in death rates. The heart is the most important organ in the human body, second only to the brain, and it is vital to the circulation of blood to all organs. A swift and accurate diagnostic method is imperative to mitigate the high mortality associated with cardiac disorders. Predicting the incidence of these disorders holds paramount importance in the medical domain. In the contemporary era, machine learning techniques prove invaluable for predicting and automating the interpretation of extensive and intricate datasets in various fields, including medicine. This research introduces a statistical model for heart disease, aiding medical examiners and cardiac practitioners in forecasting based on fundamental aspects of a patient's health history. The model, constructed using a Decision Tree, achieves an impressive accuracy of approximately 97%. The crucial functions of machine learning, data analytics, and data mining highlight how important these technologies are to improving the diagnosis and treatment of cardiac disorders.