Heart Disease Risk Assessment Using Machine Learning Algorithms

Abstract-Modern technology is changing the healthcare land- scape,and perhaps no greater impact is being made in the diagno- sis and prediction of heart disease. Inthis research work, machine learning (ML) models are applied to predict the probability of heart disease for the individual patient based on personal- related features such as age, blood pressure, cholesterol levels, and life experience information. Although several studies have implemented ML techniques, there are still challenges in limited datasets, accuracy, andinterpretability of the models used. The proposed system is intuitive as compared to otherswhere health information from the users is entered and risk is returned. If the model predicts a high risk, it recommends that the individ- ual seea health care provider. Night trip focuses on accessibility with this tool available to the generalpublic as well as medical doctors, uniting personal health with professional diagnostics. The system enhances prediction reliability responsively toheart disease prevention by harnessing the combined strengths of multiple models.