Abstract
OBJECTIVE: To understand the global trends in depression and identify potential early risk factors for its detection. METHODS: This study is the first to integrate the 2021 Global Burden of Disease (GBD) data with machine learning techniques to explore the risk factors of adolescent depression. A machine learning-based model was constructed, and SHAP (SHapley Additive exPlanations) plots were utilized for interpretive analysis. RESULTS: From 1990 to 2021, the incidence and disability-adjusted life years (DALYs) of depression continued to rise globally among the 10-24 age group, particularly in high socio-demographic index(SDI) regions. Greenland, the United States of America, and Palestine had the highest rates of depression globally. Among the eight machine learning models evaluated, random forest (RF) proved to be the most reliable. SHAP analysis revealed that elevated levels of S100β (0.330), NSE (0.060), and PLT (0.031) significantly increased the risk of depression. CONCLUSION: Our study shows an increasing trend of depression in the global 10-24 age group. Additionally, elevated S100β, NSE, and PLT are identified as key risk factors for depression.