HEART DISEASE DIAGNOSIS USING ARTIFICIAL NEURO FUZZY INFERENCE MODEL(ANFIS)
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Abstract
This project focuses on developing an intelligent system for heart disease prediction usingAdaptiveNeuro-Fuzzy Inference Systems (ANFIS) and Genetic Algorithm (GA). Cardiovascular diseaseisaleading cause of mortality worldwide, necessitating improved diagnostic tools. The proposedsystem leverages the strengths of neural networks and fuzzy logic to handle the complexitiesanduncertainties inherent in medical data. The ANFIS model is optimized using GAto enhancetheaccuracy 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 systemtoassistclinicians in making reliable diagnoses based on a combination of key medical attributes. Futurework may involve extending the system to diagnose other complex diseases and incorporatingadditional heuristic algorithms to further refine model performance
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