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

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.