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Dealing with missing values in neural network-based diagnostic systems

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Abstract

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.

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References

  1. Solberg HE. Discriminant analysis. Crit Rev Clin Lab Sci 1978; 9: 209–242

    Google Scholar 

  2. Sharpe PK, Solberg HE, Rootwelt K, Yearworth M. Artificial neural networks in thyroid function diagnosis from in vitro laboratory tests. Clin Chem 1993; 39(11): 2248–2253

    Google Scholar 

  3. Schioler T, Grimson W, Sharpe Pet al. Automatic decision support based on voting by independent decision support systems. Proc 9th Int Conf Computing in Clinical Laboratories, Dublin, Ireland, 1992

  4. Boswell RA. HyperNewID and NewID. Technical Report TI/P2154/RAB/4/9.2, Turing Institute, 1992

  5. Grimson W. Private communication

  6. Vamplew P, Adams A. Missing values in a backpropagation neural net. Proc 3rd Australian Conf Neural Networks (ACNN92), Canberra, Australia, 1992; 64–66

  7. Schioler T, Nolan J, McNair P. Transferability of knowledge based systems. In: Adlassnig K-Pet al (eds), Lecture Notes in Medical Informatics No. 45, Medical Informatics Europe 1991. Springer-Verlag, 394–398

  8. Forsstrom J. Inductive learning of thyroid functional states using the ID3 algorithm: The effect of poor examples on the learning result. Int J Biomed Comput, 1992; 30: 57–67

    Google Scholar 

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Sharpe, P.K., Solly, R.J. Dealing with missing values in neural network-based diagnostic systems. Neural Comput & Applic 3, 73–77 (1995). https://doi.org/10.1007/BF01421959

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  • DOI: https://doi.org/10.1007/BF01421959

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