Predicting generalized anxiety disorder among Chinese depressed adolescents: an explainable machine learning approach.

ABSTRACT:

Background

The co-occurrence of depression and anxiety in adolescents is associated with a greater risk than the presence of depressive symptoms alone. Predicting anxiety disorders among depressed adolescents is critical for interventions and therapeutic tools.

Methods

We recruited 2316 depressed adolescents through the Chinese Adolescent Depression Cohort (CADC) and collected 34 predictive factors for model construction. The Light Gradient Boosting Machine (LightGBM) prediction model and Shapley Additive Explanations (SHAP) algorithm were implemented for in-depth interpretation of the predictive importance of different factors. Furthermore, chi-square automatic interaction detection (CHAID) and ordinal logistic regression were used to explore the factor interactions and val

SUBMITTER: Geng S 

PROVIDER: S-EPMC12581335 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature