Differential diagnosis of schizophrenia using decision tree analysis based on cognitive testing

ABSTRACT

Background and objectives: To explore the discriminatory ability of a decision tree model based on cognitive testing data for the differential diagnosis of schizophrenia.MethodsThis study enrolled 82 patients with schizophrenia and 82 patients with affective disorders. The cognitive function of the two groups of participants was assessed based on learning, symbol coding, digital span, trail making, and category fluency tests. The logistic regression model in the sklearn package in Python was applied to discriminate and analyse the data for all 11 variables in the MATRICS Consensus Cognitive Battery (MCCB).ResultsThe recognition rate for schizophrenia and affective disorder using all 11 variables of the MCCB was 82%.ConclusionThe logistics model based on cognitive data distinguished patients with schizophrenia from those with affective disorder. (AU)