Fuzzy Logic for Heart Disease Prediction - Muhammad Raihan Rusli; Normalisa (International Journal of Artificial Intelligence, 2026)
Cardiovascular disease remains one of the leading causes of mortality worldwide, emphasizing the importance of early and accurate risk prediction systems. This study proposes a web-based heart disease prediction system utilizing a fuzzy logic approach to effectively manage uncertainty and imprecision in clinical data. As illustrated in the system interface, the model incorporates five primary input parameters: blood pressure, blood sugar level, cholesterol level, body mass index (BMI), and family history. These parameters are transformed into linguistic variables through fuzzification and evaluated using a rule-based fuzzy inference system constructed from expert knowledge and recent research findings. The fuzzy logic framework enables flexible decision-making that closely resembles human reasoning, overcoming limitations of conventional threshold-based diagnostic methods. The system produces an interpretable heart disease risk classification, such as low or high risk, which is presented through an interactive web interface along with a prediction history feature. Based on a review of related studies published within the last five years, fuzzy logic and neuro-fuzzy models have shown strong performance, transparency, and suitability for clinical decision support. The proposed system demonstrates the practicality of integrating fuzzy logic into web applications to support early heart disease risk assessment and preventive healthcare.