Evolution of artificial neural network architecture: Prediction of depression after mania

M. F. Jefferson; N. Pendleton; C. P. Lucas; S. B. Lucas; M. A. Horan

Evolution of artificial neural network architecture: Prediction of depression after mania

M. F. Jefferson, N. Pendleton, C. P. Lucas, S. B. Lucas, M. A. Horan

Research output: Contribution to journal › Article › peer-review

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

Original language	English
Pages (from-to)	220-225
Number of pages	5
Journal	Methods of Information in Medicine
Volume	37
Issue number	3
Publication status	Published - 1998

Keywords

Artificial Neural Networks
Genetic Algorithms

Cite this

@article{05e3fb4653684086a90a569c2da589a5,

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",

keywords = "Artificial Neural Networks, Genetic Algorithms",

author = "Jefferson, {M. F.} and N. Pendleton and Lucas, {C. P.} and Lucas, {S. B.} and Horan, {M. A.}",

year = "1998",

language = "English",

volume = "37",

pages = "220--225",

journal = "Methods of Information in Medicine",

issn = "0026-1270",

publisher = "Georg Thieme Verlag",

number = "3",

}

TY - JOUR

T1 - Evolution of artificial neural network architecture: Prediction of depression after mania

AU - Jefferson, M. F.

AU - Pendleton, N.

AU - Lucas, C. P.

AU - Lucas, S. B.

AU - Horan, M. A.

PY - 1998

Y1 - 1998

N2 - 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

AB - 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

KW - Artificial Neural Networks

KW - Genetic Algorithms

M3 - Article

SN - 0026-1270

VL - 37

SP - 220

EP - 225

JO - Methods of Information in Medicine

JF - Methods of Information in Medicine

IS - 3

ER -

Evolution of artificial neural network architecture: Prediction of depression after mania

Abstract

Keywords

Fingerprint

Cite this