Machine learning-decision tree classifiers in psychiatric assessment: An application to the diagnosis of major...

This work illustrates the advantages of using machine learning classifiers in psychiatric assessment. Machine learning-decision trees (ML-DTs) represent a new approach to scoring and interpreting psychodiagnostic test data that allows for increasing assessment accuracy and efficiency. The approach is outlined in an easy yet detailed way, and its application is illustrated on real psychodiagnostic test data. Specifically, cross-sectional data concerning nonclinical and clinical Japanese populations were taken from a panel registered with an internet survey company. Responses to the Patient Health Questionnaire-9 (PHQ-9) underwent receiver operating characteristic (ROC) curve, DSM algorithm, and ML-DT analyses. The results showed greater diagnostic accuracy for ML-DT (0.71-0.75) compared with the DSM algorithm (0.69) and ROC curves (0.70-0.71). Moreover, ML-DT enabled classifying participants as having or not having a diagnosis of depression using, on average, the information from 2.99 out of 9 items (SD = 1.35). The application showed that ML-DTs can provide information of high clinical value to integrate traditional psychometric methods. The resulting assessments are informative, accurate, and efficient.