IMPLEMENTATION OF FUZZY INFERENCE SYSTEM (FIS) FOR CARDIOVASCULAR DISEASES PREDICTION

Abstract

Abstract- Cardiovascular diseases (CVDs) continue to be a leading cause of mortality worldwide. Early and accurate prediction of CVDs risk is crucial for effective prevention and management. This study presents the implementation of a Fuzzy Inference System (FIS) for predicting suseptibility cardiovascular diseases. The implementation of FIS for the prediction of cardiovascular disease is by determining the membership function for risk factors that influence the susceptibility of the disease. The FIS developed in this study integrates five risk factors, including age, systolic blood pressure, diastolic blood pressure, blood sugar and cholesterol and one output parameter CVDs prediction. The FIS method used Mamdani with 162 rules. Real-world patient data diagnosed with cardiovascular disease is used to train and validate the FIS. Validity testing produces 100% valid data. Testing is carried out using patient data. The method used to validate the results of the FIS implementation is by distributing questionnaires to several paramedics.. These findings provide insights into further refinements of CVD risk modeling and potential applications in clinical practice.