An intelligent data mining model for diabetic patients data using hybrid adaptive neuro-fuzzy inference system

Abstract

Proper diagnosis of diabetic based on the patient’s medical analysis results is an important factor. Data mining helps in analyzing such data includes complex meaningful terms to diagnosis and supports the patients to take remedy action based on the accurate results. The proposed model is a data mining model for analyzing diabetic patient’s data using sugeno type adaptive neuro fuzzy inference system with principle component analysis as a hybrid system. The experimental model validated through 200 different data obtained from health clinic with 25 different attributes. The proposed model classifies the data with accuracy of 94.6% where as conventional rough set and k means clustering model produces less classification accuracy of 74.5% and 77.6%.