Enhancing Healthcare with Early Heart Disease Prediction

Heart disease, a widespread ailment that affects communities all over the world, is one of the most important global health concerns. The goal of this essay is to construct a machine learning model that can detect cardiac disease in its early stages because early detection is crucial for saving lives. The importance of this project rests in its potential to help the general population by enabling early intervention before heart issues worsen, potentially lowering the need for pricy medical procedures and treatments in hospitals and clinics. For a very long time, cardiac disease prediction and detection have been extremely difficult for healthcare professionals. These problems frequently result from lifestyle choices like bad eating habits, binge drinking, smoking, high blood pressure, raised cholesterol, diabetes, obesity, stress, and a sedentary lifestyle. As a result, creating a reliable prediction model has enormous potential for solving this important healthcare problem. Different supervised machine learning techniques have been used to predict cardiac disease, with an excellent accuracy rate of 94%.the performance of the proposed technique is evaluated on a real dataset known as mortality-rate-heart-patient-hospital.