Extracting rules from composite neural networks for medical diagnostic problems

Mu Chun Su, Hsiao Te Chang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Recently, neural networks have been applied to many medical diagnostic problems because of their appealing properties, robustness, capability of generalization and fault tolerance. Although the predictive accuracy of neural networks may be higher than that of traditional methods (e.g., statistical methods) or human experts, the lack of explanation from a trained neural network leads to the difficulty that users would hesitate to take the advise of a black box on faith alone. This paper presents a class of composite neural networks which are trained in such a way that the values of the network parameters can be utilized to generate If-Then rules on the basis of preselected meaningful coordinates. The concepts and methods presented in the paper are illustrated through one practical example from medical diagnosis.

Original languageEnglish
Pages (from-to)253-263
Number of pages11
JournalNeural Processing Letters
Issue number3
StatePublished - 1998


  • Expert system
  • Genetic algorithms
  • Medical diagnosis
  • Neural networks
  • Rule extraction


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