Introduction to the theory of neural computation
Introduction to the theory of neural computation
Improving the convergence of the back-propagation algorithm
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Neural expert system using fuzzy teaching input and its application to medical diagnosis
Information Sciences—Applications: An International Journal
Fuzzy MLP based expert system for medical diagnosis
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
A penalty-function approach for pruning feedforward neural networks
Neural Computation
Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Neural Networks in Computer Intelligence
Neural Networks in Computer Intelligence
Symbolic Interpretation of Artificial Neural Networks
IEEE Transactions on Knowledge and Data Engineering
Paper: Multiple disorder diagnosis with adaptive competitive neural networks
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Interference-less neural network training
Neurocomputing
Generation of comprehensible hypotheses from gene expression data
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Book review: Artificial Neural Networks in Biomedicine
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Computer Methods and Programs in Biomedicine
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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.