Intelligent Heart Disease Prediction System with Symptom-Based Test Recommendation

Heart disease remains one of the leading causes of mortality worldwide, often due to late diagnosis and lack of early symptom awareness. Many existing prediction systems rely mainly on clinical test results and do not assist patients at the initial symptom stage. This paper proposes an Intelligent Heart Disease Prediction System that bridges this gap by combining symptom analysis, test recommendation, and machine learning–based risk prediction. The system first evaluates user-reported symptoms, suggests appropriate medical tests, and then predicts the likelihood of heart disease using trained classification models. A user-friendly interface allows patients to interact with the system easily, while healthcare professionals can use it as a pre-screening support tool. Experimental evaluation shows that the proposed system improves early risk detection accuracy and supports timely medical intervention. This approach can enhance patient awareness, reduce delayed diagnosis, and contribute to preventive healthcare.