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 re

cently 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 data set con

sists of nine measurements collected from patients in a Japanese hospital and these

measurements have continuous values. NeuroLinear generates piece-wise linear dis

criminant functions for this data set. The continuous measurements have previously

been discretized by domain experts. NeuroRule is applied to the discretized data

set 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 data set to be slightly more accurate and more concise than the

rules generated by NeuroRule from the discretized data set.

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