Published Paper Details:

: Heart disease is a leading cause of mortality worldwide, necessitating effective and timely diagnosis. This study presents a novel approach to predicting heart disease using advanced machine learning techniques and data-driven insights. The system is designed for the identification of heart disease, leveraging the performance of various machine learning classifiers on selected features. Predictive models including Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM) are employed to identify heart disease. The effectiveness of these classifiers is evaluated to determine the most accurate method for heart disease detection. Additionally, the system provides patients with information about the nearest doctor, facilitating rapid access to medical diagnosis and treatment. This integrated approach aims to enhance early detection and intervention for heart disease, ultimately improving patient outcomes and reducing the burden on healthcare systems.