Performance Analysis of Neural Network Based Classifiers for the Prediction of Diabetes

Abstract

Diabetes is the most harmful diseases to consider in recent years since it causes severe damage to human beings in the form of elevated sugar levels. In a recent survey, it was projected that over 385 million public were affected in the entire world. Several investigators were conducted various experiments for prediction of diabetes using various classification techniques. This paper deals with a neural classifier based prediction system to recognize diabetes. Two learning algorithms namely, Levenberg Marquardt back propagation (LM), and gradient descent with variable learning rate are is investigated for different architecture and the best architecture with good accuracy was identified. The data are together from the Government Hospital of Pondicherry and it is formed as a database. Totally, datasets of 500 have been together, out of which 350 datasets as training sets for training process and 150 datasets as testing sets for the testing process. The recognition accuracy is obtained. For comparison, k-Nearest Neigourhood and the K- nearest neighbor and Radial Basis Function (RBF) network are also implemented and it is trained and tested with the same datasets. The result shows that Neural Network outperforms well with other classifiers.