Neural Computing & Applications

, Volume 3, Issue 2, pp 73–77 | Cite as

Dealing with missing values in neural network-based diagnostic systems

  • P. K. Sharpe
  • R. J. Solly


Backpropagation neural networks have been applied to prediction and classification problems in many real world situations. However, a drawback of this type of neural network is that it requires a full set of input data, and real world data is seldom complete. We have investigated two ways of dealing with incomplete data — network reduction using multiple neural network classifiers, and value substitution using estimated values from predictor networks — and compared their performance with an induction method. On a thyroid disease database collected in a clinical situation, we found that the network reduction method was superior. We conclude that network reduction can be a useful method for dealing with missing values in diagnostic systems based on backpropagation neural networks.


Backpropagation Classification Decision support Neural networks Thyroid disease 


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Copyright information

© Springer-Verlag London Limited 1995

Authors and Affiliations

  • P. K. Sharpe
    • 1
  • R. J. Solly
    • 1
  1. 1.The Transputer CentreUniversity of the West of EnglandFrenchayUK

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