Enhancing prediction of major depressive disorder onset in adolescents: A machine learning approach.
Journal:
Journal of psychiatric research
PMID:
39823922
Abstract
Major Depressive Disorder (MDD) is a prevalent mental health condition that often begins in adolescence, with significant long-term implications. Indicated prevention programs targeting adolescents with mild symptoms have shown efficacy, yet the methods for identifying at-risk individuals need improvement. This study aims to evaluate the utility of Partial Least Squares Regression (PLSR) in predicting the onset of MDD among non-depressed adolescents, compared to traditional screening methods. The study recruited 1462 Portuguese adolescents aged 13-16, who were assessed using various self-report measures and followed for two years. Participants were randomly divided into training (70%, N = 1023) and testing (30%, N = 439) samples. PLSR models were developed to predict the occurrence of a major depressive episode (MDE) within two years, using 331 variables. The model's performance was compared to the Children's Depression Inventory (CDI) in predicting MDE onset. The best-fitting PLSR model with two components explained 19.1% and 16.9% of the variance in the training and testing samples, respectively, significantly outperforming the CDI, which explained 7.7% of the variance. The area under the ROC curve was 0.78 for PLSR, compared to 0.71 for CDI. An empirically derived cut-off point was used to create dichotomous risk categories, and it showed a significant difference in MDE rates between predicted high-risk and low-risk groups. The balanced accuracy of the PLSR model was 0.77, compared to 0.65 for the CDI method. The PLSR model effectively identified adolescents at risk for developing MDD, demonstrating superior predictive power over the CDI. This study supports the potential utility of ML techniques (e.g., PLSR) in enhancing early identification and prevention efforts for adolescent depression.