Evolution of artificial neural network architecture: prediction of depression after mania.
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.
Jefferson
M F
MF
University of Manchester Department of Geriatric Medicine, Hope Hospital, Salford, UK. jefferson@btinternet.com
Pendleton
N
N
Lucas
C P
CP
Lucas
S B
SB
Horan
M A
MA
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