Cardiovascular diseases, including heart disease, remain a significant global health concern, necessitating early and accurate risk assessment for effective prevention and intervention strategies. In this research paper, we present a comprehensive study on predictive modeling of heart disease risk using a data mining approach coupled with classification techniques in machine learning. Our study revolves around the application of advanced data mining methods to extract valuable patterns and insights from a diverse dataset comprising medical attributes, lifestyle factors, and patient histories. Leveraging the power of
classification algorithms, we aim to create predictive models capable of identifying individuals at risk of heart disease.