Implementation of Inference Engine in Adaptive Neuro Fuzzy Inference System to Predict and Control the Sugar Level in Diabetic Patient

Abstract Fuzzy logic in medical diagnosis is a promising technique usually involves careful examination of a patient to check the presence and strength of some features relevant to a suspected disease in order to take a decision whether the patient suffers from that disease or not. The present work introduces implementation of a simple and effective methodology to develop fuzzy Inference systems for diabetic’s diagnosis to estimate the risk factor value of a human being with respect to ranges of sugar level such as Fasting, After meal, before meal and function value of Total Energy Expenditure. The main goal of the paper is to develop data mining techniques to support decision making and to control the controllable risk factors and also overcome the other parts of organs highly affected by diabetes and which in turn reduces the risk of the patients. By applying the powerful technique of ANFIS based on Sugeno method. The research methodology diagram of the proposed research is classified into two levels. In first level, the research can be analyse the BMR, TEE and diet taken in time bases of fluctuation in different time (Fasting, before meal, after meal, bed time), then analyse the scoring sugar level of patient risk 6). In second level, to fixing an insulin range for reducing the risk of patient health based on the score of sugar level in first level. The result shows, how a fuzzy logic controller is used to control the controllable risk factors to regularize the blood sugar level and also how a patient can control the contributing factors of inactivity of dosage of insulin, to find the life time postponement of other organs affected by diabetes, to protect the patient from risk of blood sugar level.