Abstract
Feed forward back-propagation artificial neural networks (ANNs) have been used to predict outcomes in a variety of biomedical settings [1],[2]. Their advantages and disadvantages compared with standard statistical methods (SSMs) relate to different appraisals of three major problems that are common to all methods for estimation: (1) agreement, (2) stability and (3) transparency. In this chapter we consider these problems and propose a method which addresses some of them using procedures called genetic algorithms. Our arguments are illustrated by comparing the performance of the various prediction methods in predicting the occurrence of depression after mania [3].
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Jefferson, M.F., Pendleton, N., Lucas, S.B. (2000). Genetic Evolution of Neural Network Architectures. In: Lisboa, P.J.G., Ifeachor, E.C., Szczepaniak, P.S. (eds) Artificial Neural Networks in Biomedicine. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0487-2_4
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DOI: https://doi.org/10.1007/978-1-4471-0487-2_4
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