Deepa, R and Mayilvaganan, M (2017) Implementation of Inference Engine in Adaptive Neuro Fuzzy Inference System to Predict and Control the Sugar Level in Diabetic Patient. International Journal of Engineering and Technology, 9 (2). ISSN 0975-4024

[thumbnail of Dr.R.Deepa_Implementation of Inference Engine in.pdf] Text
Dr.R.Deepa_Implementation of Inference Engine in.pdf

Download (562kB)


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.

Item Type: Article
Uncontrolled Keywords: ANFIS, Fuzzy controller, Sugeno method, BMR, TEE, Activity factor.
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 02 Jan 2023 09:23
Last Modified: 02 Jan 2023 09:23

Actions (login required)

View Item
View Item