Diagnosis of diabetes by using adaptive neuro fuzzy inference systems

Abstract

Most of discoveries indicate that the best way to overcome diabetes is to prevent the risks of diabetes before becoming a diabetic. With this opinion, we would like to find a way to estimate diabetes risk, according to some variables such as age, total cholesterol, gender or shape of the body. Due to having fuzzy input and output (glucose rate) values and because of that dependent variable have more than 2 values (unlike binary logic), ANFIS and Multinomial Logistic Regression should be executed for comparison. Then the results were benchmarked. As a result, in case of that there is a system which contains fuzzy inputs and output, ANFIS gives better results than Multinomial Logistic Regression for diabetes diagnosis. ©2009 IEEE. © 2010 Elsevier B.V., All rights reserved.