Identification of Diabetes Disease Using Committees of Neural Network-Based Classifiers

Abstract

Diabetes mellitus is one of the most serious health challenges in both developing and developed countries. In this paper, we present a design of a classifier committee for the detection of diabetes disease based on the Pima Indian diabetic database from the UCI machine learning repository. The proposed method uses multi-layer perceptron (MLP) and cascade-forward back propagation network (CFBN) predictors as base classifiers. The combined committee is based on varying the parameters related to both the design and the training of the neural network classifiers. Our experimental evaluation confirms that the derived approach provides a robust classification system, and yields classification accuracies of 95.31 and 96.88 % based on using combined MLP and combined CFBN classifiers respectively. The experimental results obtained thus show that the proposed classifier committee can form as useful basis for automatic diagnosis of diabetes.