Document Type

Conference Proceeding

Publisher

IEEE

Faculty

Computing, Health and Science

School

Engineering and Mathematics

RAS ID

2056

Comments

This conference paper was originally published as: Arulampalam, G. , & Bouzerdoum, A. (2001). Application of shunting inhibitory artificial neural networks to medical diagnosis. Proceedings of 7th Australian and New Zealand Intelligent Information Systems Conference. (pp. 89 - 94 ). Australia. IEEE. Original article available here

Abstract

Shunting inhibitory artificial neural networks (SIANNs) are biologically inspired networks in which the neurons interact among each other via a nonlinear mechanism called shunting inhibition. Since they are high-order networks, SIANNs are capable of producing complex, nonlinear decision boundaries. In this article, feedforward SIANNs are applied to several medical diagnosis problems and the results are compared with those obtained using multilayer perceptrons (MLPs). First, the structure of feedforward SIANNs is presented. Then, these networks are applied to some standard medical classification problems, namely the Pima Indians diabetes and Wisconsin breast cancer classification problems. The SIANN performance compares favourably with that of MLPs. Moreover, some problems with the diabetes data set are addressed and a reduction in the number of inputs is investigated.

DOI

10.1109/ANZIIS.2001.974056

Included in

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Link to publisher version (DOI)

10.1109/ANZIIS.2001.974056