Development of a Fuzzy Inference System for Early Diagnosis of Heart Disorders

The early detection of heart disorders is crucial for effective treatment and improved patient outcomes. This study presents the development of a Fuzzy Inference System (FIS) designed to assist in the early diagnosis of heart-related conditions by evaluating imprecise and overlapping clinical symptoms. The system uses three primary input parameters—blood pressure, cholesterol levels, and chest pain type—which are fuzzified into linguistic variables and assessed using a comprehensive rule base of 27 fuzzy logic rules. The inference mechanism employs Mamdani-style fuzzy reasoning, and defuzzification is carried out using the centroid method to yield a crisp risk score. Simulated case studies based on standard medical thresholds demonstrate the system’s diagnostic alignment with physician evaluations. The model achieved 100% accuracy, sensitivity, and specificity in the test dataset, confirming its reliability and clinical applicability. The proposed FIS offers an interpretable, intelligent diagnostic framework that can support healthcare professionals in making timely and informed decisions regarding cardiovascular risk assessment.