HEART DISEASE DIAGNOSIS USING ARTIFICIAL NEURO FUZZY INFERENCE MODEL(ANFIS)

ABSTRACT

This project focuses on developing an intelligent system for heart disease prediction using Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Genetic Algorithm (GA). Cardiovascular disease is a leading cause of mortality worldwide, necessitating improved diagnostic tools. The proposed system leverages the strengths of neural networks and fuzzy logic to handle the complexities and uncertainties inherent in medical data. The ANFIS model is optimized using GA to enhance the accuracy of predictions. The system was tested on the UCI Cleveland heart disease dataset, achieving a satisfactory accuracy rate of 91.25%. This indicates the potential of the system to assist clinicians in making reliable diagnoses based on a combination of key medical attributes. Future work may involve extending the system to diagnose other complex diseases and incorporating additional heuristic algorithms to further refine model performance.