This document describes a study that implemented an Adaptive Neuro Fuzzy Inference System (ANFIS) for malaria diagnosis at Kwesimintsim Polyclinic in Ghana. The traditional diagnosis method is prone to inaccuracies due to human factors like fatigue and stress. ANFIS was trained on malaria patient data to diagnose patients more efficiently than the traditional method with minimal error. ANFIS integrates the learning ability of neural networks and the inference ability of fuzzy logic for modeling and predicting input-output relationships from data. The study loaded training and checking patient data into ANFIS, generated a fuzzy inference system, optimized parameters using a hybrid learning method, and achieved more accurate malaria diagnoses than the traditional guesswork-based approach

Implementation of Adaptive Neuro Fuzzy Inference System For Malaria Diagnosis (Case Study: Kwesimintsim Polyclinic) PDF

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