Predicting 3-year persistent or recurrent major depressive episode using machine learning techniques

Background: The identification of predictors of recurrence and persistence of depressive episodes in major depressive disorder (MDD) can be important to inform clinicians and collaborate to clinical decisions. Objective: The aim of the present study is to predict recurrent or persistent depressive episodes, in addition to predicting severe recurrent or persistent depressive episodes using a machine learning method. Methods: This is a prospective cohort study with three years of follow-up. Individuals diagnosed with MDD in the first phase of the study (2012–2015) were evaluated in the second phase (2012–2015). The sociodemographic, clinical, comorbid disorders and substance use variables were used as predictors in all predictive models. Initially, the first model predicted recurrence/persistence, including subjects of any severity of depression level. The second model predicted recurrence/persistence depression as the first model, although it was trained with severely depressed subjects and those without indicative for depression. The third model predicted severe depression among depressed patients. Results: Area under the curve (AUC) values ranged from 0.65 to 0.81, and accuracies ranged from 62% to 71%. Psychiatric comorbidities, substance abuse/dependence, and family medical history were important features in all three models. Limitation: The time between baseline and the second phase of the study was approximately three years, making it difficult to detect depressive symptoms during this time frame. Also, age at depression onset and number of episodes were not included in the model due to the large number of missing data. Conclusions: In conclusion, this study adds new information that can help health professionals both in their clinical practice and in public services.