Document Type
Conference Proceeding
Publisher
IEEE
Faculty
Faculty of Computing, Health and Science
School
School of Engineering and Mathematics
RAS ID
2056
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
Access Rights
free_to_read
Comments
This is an Author's Accepted Manuscript of: 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. Available here
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