Title: GA_RBF NN: a classification system for diabetes

Authors: Dilip Kumar Choubey; Sanchita Paul

Addresses: Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India ' Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India

Abstract: The modern society is prone to many life-threatening diseases, which if diagnosed early, can be easily controlled. The implementation of a disease diagnostic system has gained popularity over the years. The main aim of this research is to provide a better diagnosis of diabetes disease. There are already several existing methods, which have been implemented for the diagnosis of diabetes dataset. Here, the proposed approach consists of two stages: in first stage Genetic algorithm (GA) used as an attribute (feature) selection which reduces 4 attributes among 8 attributes, and in the second stage Radial Basis Function Neural Network (RBF NN) has been used for classification on selected attributes among all the attributes. The experimental results show the performance of the proposed methodology on Pima Indian Diabetes Dataset (PIDD) and provide better classification for diagnosis of diabetes patients on PIDD. GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can also be used for other kinds of medical diseases.

Keywords: PIDD; genetic algorithms; radial basis function; RBF neural networks; diabetes diagnosis; attribute selection; classification; ROC; receiver operating curve; accuracy; precision; recall; F-measure; confusion matrix; feature selection; diabetics.

DOI: 10.1504/IJBET.2017.082229

International Journal of Biomedical Engineering and Technology, 2017 Vol.23 No.1, pp.71 - 93

Received: 21 Dec 2015
Accepted: 03 Apr 2016
Published online: 13 Feb 2017 *

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