title: Evolution of artificial neural network architecture: prediction of depression after mania.
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.
authors.affiliation: University of Manchester Department of Geriatric Medicine, Hope Hospital, Salford, UK. jefferson@btinternet.com
authors.forename: M F
authors.initials: MF
authors.lastname: Jefferson
authors.forename: N
authors.initials: N
authors.lastname: Pendleton
authors.forename: C P
authors.initials: CP
authors.lastname: Lucas
authors.forename: S B
authors.initials: SB
authors.lastname: Lucas
authors.forename: M A
authors.initials: MA
authors.lastname: Horan
mesh_terms: D000465:Algorithms; D001714:Bipolar Disorder / Q000175:diagnosis* / Q000235:genetics; D003866:Depressive Disorder / Q000175:diagnosis* / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D012306:Risk