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Methods Inf Med 1998; 37(03): 220-225
DOI: 10.1055/s-0038-1634532
Original Article
Schattauer GmbH

Evolution of Artificial Neural Network Architecture: Prediction of Depression after Mania

M. F. Jefferson
1   University of Manchester Department of Geriatric Medicine, Clinical Sciences Building, Hope Hospital, Salford, UK
2   University of Manchester Department of Psychiatry, Manchester Royal Infirmary, Oxford Road, Manchester, UK
,
N. Pendleton
1   University of Manchester Department of Geriatric Medicine, Clinical Sciences Building, Hope Hospital, Salford, UK
,
C. P. Lucas
3   Columbia State University, Department of Psychiatry, New York, USA
,
S. B. Lucas
4   University of Manchester Department of Medical Biophysics, Stopford Building, Oxford Road, Manchester, UK
,
M. A. Horan
1   University of Manchester Department of Geriatric Medicine, Clinical Sciences Building, Hope Hospital, Salford, UK
› Author Affiliations
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Publication History

Publication Date:
14 February 2018 (online)

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Abstract

Artificial neural networks (ANNs) are compared to standard statistical methods for outcome prediction in biomedical problems. A general method for using genetic algorithms to "evolve" ANN architecture (EANN) is presented. Accuracy of logistic regression, a fully interconnected ANN, and an EANN for predicting depression after mania are examined. All methods showed very good agreement (training set accuracy, chi-square all p <0.01). However, significant differences were found for stability (test set accuracy); logistic regression being the most unstable and EANN being significantly more stable than a fully interconnected ANN (McNemar p <0.01). We conclude that the EANN method enhances ANN stability. This approach may have particular relevance for biomedical prediction problems, such as predicting depression after mania.

Keywords

Artificial Neural Networks - Genetic Algorithms
 

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