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Application of a radial basis function neural network for diagnosis of diabetes mellitus

Venkatesan, P and Anitha, S (2006) Application of a radial basis function neural network for diagnosis of diabetes mellitus. Current Science, 91 (9). pp. 1195-1199. ISSN 0011-3891

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Abstract

In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network with one hidden layer, namely. Radial Basis Function (RBF) neural network. It uses relatively smaller number of locally tuned units and is adaptive in nature. RBFs are suitable for pattern recognition and classification. Performance of the RBF neural network was also compared with the most commonly used multilayer perceptron network model and the classical logistic regression. Diabetes database was used for empirical comparisons and the results show that RBF network performs better than other models.

NIRT Creators:
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Item Type: Article
Uncontrolled Keywords: Artificial neural network, logistic regression, multilayer perceptron, radial basis function, supervised learning
Subjects: Tuberculosis > Biostatistics
Divisions: Statistics
Depositing User: Dr. Rathinasabapati R
Date Deposited: 20 Dec 2013 10:16
Last Modified: 09 Mar 2016 06:36
URI: http://eprints.nirt.res.in/id/eprint/789

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