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

Utilizing an explainable machine learning approach enables us to identify the risk and protective factors for anxiety disorders among depressed adolescents. The SHAP analysis results suggest that depression severity was the most important predictor for co-morbid anxiety. CHAID decision tree further identified risk subgroups. These findings suggest that clinical workers take into consideration the above risk and protective factors as well as their interactions to develop appropriate therapies for the prevention of comorbid anxiety with depression.