Open AccessNeural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees.
Author(s) -
Gilbert Reibnegger,
G. Weiss,
Gabriele Werner-Felmayer,
G Judmaier,
Helmut Wächter
Publication year - 1991
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.88.24.11426
Subject(s) - linear discriminant analysis , artificial neural network , jackknife resampling , artificial intelligence , computer science , robustness (evolution) , linear regression , regression , discriminant , machine learning , pattern recognition (psychology) , regression analysis , field (mathematics) , data mining , statistics , mathematics , biology , biochemistry , estimator , pure mathematics , gene
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.