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American Journal of Epidemiology Vol. 147, No. 12: 1112-1122
Copyright © 1998 by The Johns Hopkins University School of Hygiene and Public Health

other

Epidemiologic Interpretation of Artificial Neural Networks

Mei-Sheng Duh1, Alexander M. Walker1, and John Z. Ayanian2

1Department of Epidemiology, Harvard School of Public Health Boston, MA
2Division of General Medicine, Brigham and Women's Hospital, and Department of Health Care policy, Harvard Medical School Boston, MA

Reprint requests to Dr. Alexander M. Walker, Department of Epidemiology, Harvard School of Public Health, 677 Huntington Ave., Boston, MA 02115.

Multilayer neural networks have been faulted for functioning as "black boxes" and for failing to assess the relative importance of the input factors. The aim of this paper is to illustrate how neural networks can classify individuals. The authors investigated the role of weights in the formation of neural networks‘ decision surfaces and decision regions. The data used were from a case-control study. Two strong determinants of case status were used as input "neurons." Zero, three, and five hidden neurons were used to explore the effect of the number of hidden neurons on the decision surfaces and regions. Mapping of input and output spaces revealed that three hidden neurons were insufficient to fully discriminate cases from controls. Five hidden neurons may be optimal, but at the cost of possible over-fitting. The more complex neural networks were very effective at defining regions of uniform risk in the plane of the initial covariates, and at assigning risk levels. The authors speculate that neural networks will prove useful in epidemiologic problems that require pattern recognition or complicated classification techniques, and that they will be unfavorable in problems that involve distinct effects of distinguishable predictors. Am J Epidemiol 1998; 147: 1112–22.

logistic regression; neural networks (computer); odds ratio; statistics


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