A genetic algorithm system to find symbolic rules for diagnosis of depression

Author

Chapman, Christopher N. ; Deaton, Lana ; Harris, Angela ; Robinson, Nova

Author_Institution

Dept. of Psychol., Tulsa Univ., OK, USA

Volume

1

fYear

1999

fDate

1999

Abstract

A machine learning method is proposed for automatically finding psychiatric diagnostic rules. It is proposed that a genetic algorithm (GA) system can find symbolic, easily readable rules that could be used by psychiatric clinicians. Diagnosis of major depressive disorder is considered. A sample of 320 subjects with symptom information and pre-assigned diagnosis is used to train a GA model and two other statistical models, discriminant analysis and logistic regression. Each model is able correctly to classify more than 91% of cases. The GA model performs best of the three methods and yields readable, non-numeric rules

Keywords

genetic algorithms; learning systems; medical diagnostic computing; patient diagnosis; psychology; statistical analysis; automatic psychiatric diagnostic rule finding; depression diagnosis; discriminant analysis; genetic algorithm system; logistic regression; machine learning method; major depressive disorder diagnosis; non-numeric rules; pre-assigned diagnosis; psychiatric clinicians; readable rules; statistical models; symbolic rules; symptom information; Educational institutions; Equations; Genetic algorithms; Information analysis; Learning systems; Logistics; Machine learning; Mental disorders; Performance loss; Psychology;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.781917

Filename

781917