Performance Evaluation of AI Models in Early Heart Disease Diagnosis

Abstract

Machine learning is increasingly being utilized throughout various stages of healthcare, particularly in predicting conditions like locomotor disorders and heart disease. Treating heart disease through therapies and surgeries in hospitals and clinics can be prohibitively expensive. As a result, early detection becomes crucial, allowing individuals to take preventive measures before more severe health complications occur. Heart disease is a widespread concern today, often linked to factors such as excessive alcohol use, smoking, and lack of physical exercise. Over time, machine learning has shown great potential in decision-making and prediction by leveraging large datasets from the healthcare sector. In this project, we developed models to predict heart disease by analyzing various heart-related factors of affected individuals, employing machine learning techniques. Early diagnosis can help high-risk patients adopt lifestyle changes, potentially preventing complications and representing a major step forward in medical care.