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Neural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees.

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Affiliations

  • 1. Institute for Medical Chemistry and Biochemistry, University of Innsbruck, Austria.
      Authors
    • Reibnegger G 1
    (1 author)

ORCIDs linked to this article

Proceedings of the National Academy of Sciences of the United States of America, 01 Dec 1991, 88(24):11426-11430
https://doi.org/10.1073/pnas.88.24.11426 PMID: 1763057 PMCID: PMC53148

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Abstract 

Successful applications of neural network architecture have been described in various fields of science and technology. We have applied one such technique, error back-propagation, to a medical classification problem stemming from clinical chemistry, and we have compared the performance of two different neural networks with results obtained by conventional linear discriminant analysis or by the technique of classification and regression trees. The results obtained by the various models were tested for robustness by jackknife validation ("leave n out" method). Compared with the two other techniques, neural networks show a unique ability to detect features hidden in the input data which are not explicitly formulated as input. Thus, neural network techniques appear promising in the field of clinical chemistry, and their application, particularly in situations with complex data structures, should be investigated with more emphasis.

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Proc Natl Acad Sci U S A. 1991 Dec 15; 88(24): 11426–11430.
PMCID: PMC53148
PMID: 1763057

Neural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees.

G Reibnegger, G Weiss, G Werner-Felmayer, G Judmaier, and H Wachter
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Abstract

Successful applications of neural network architecture have been described in various fields of science and technology. We have applied one such technique, error back-propagation, to a medical classification problem stemming from clinical chemistry, and we have compared the performance of two different neural networks with results obtained by conventional linear discriminant analysis or by the technique of classification and regression trees. The results obtained by the various models were tested for robustness by jackknife validation ("leave n out" method). Compared with the two other techniques, neural networks show a unique ability to detect features hidden in the input data which are not explicitly formulated as input. Thus, neural network techniques appear promising in the field of clinical chemistry, and their application, particularly in situations with complex data structures, should be investigated with more emphasis.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.
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