Heart-Disease System: A Rule-Based Prediction Model for Heart Disease Symptoms and Causes
Authors:-Chaudhari Khushikumari
Abstract-Heart disease remains one of the leading causes of mortality worldwide. Early detection and prevention are crucial to reducing the risks associated with cardiovascular diseases. This paper presents a rule-based predictive system for heart disease diagnosis. The system leverages patient data, including medical history, lifestyle factors, and clinical symptoms, to provide an accurate assessment of potential heart disease risks. Implemented in Python, the system follows a structured decision-making approach based on medical guidelines and expert knowledge. The proposed system aids in decision-making for healthcare professionals and individuals by evaluating multiple health indicators to predict heart disease risk. This research paper discusses the dataset used, feature selection, rule formulation, system architecture, and evaluation criteria. The study demonstrates that expert-driven approaches can enhance early detection and contribute to improved patient outcomes.
