Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0933-3657(00)00064-6
Title: A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders
Authors: Hayashi, Y.
Setiono, R. 
Yoshida, K.
Keywords: Hepatobiliary disorders
Network pruning
Neural networks
NeuroLinear
NeuroRule
Rule extraction
Issue Date: 2000
Source: Hayashi, Y., Setiono, R., Yoshida, K. (2000). A comparison between two neural network rule extraction techniques for the diagnosis of hepatobiliary disorders. Artificial Intelligence in Medicine 20 (3) : 205-216. ScholarBank@NUS Repository. https://doi.org/10.1016/S0933-3657(00)00064-6
Abstract: Neural networks have been widely used as tools for prediction in medicine. We expect to see even more applications of neural networks for medical diagnosis as recently developed neural network rule extraction algorithms make it possible for the decision process of a trained network to be expressed as classification rules. These rules are more comprehensible to a human user than the classification process of the networks which involves complex nonlinear mapping of the input data. This paper reports the results from two neural network rule extraction techniques, NeuroLinear and NeuroRule applied to the diagnosis of hepatobiliary disorders. The dataset consists of nine measurements collected from patients in a Japanese hospital and these measurements have continuous values. NeuroLinear generates piece-wise linear discriminant functions for this dataset. The continuous measurements have previously been discretized by domain experts. NeuroRule is applied to the discretized dataset to generate symbolic classification rules. We compare the rules generated by the two techniques and find that the rules generated by NeuroLinear from the original continuously valued dataset to be slightly more accurate and more concise than the rules generated by NeuroRule from the discretized dataset. Copyright (C) 2000 Elsevier Science B.V.
Source Title: Artificial Intelligence in Medicine
URI: http://scholarbank.nus.edu.sg/handle/10635/42876
ISSN: 09333657
DOI: 10.1016/S0933-3657(00)00064-6
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