Predicting Depression Risk Among Chinese College Students From Childhood Adversity and Recent Stress Exposure Using Interpretable Machine Learning.

Previous research has shown that both early-life stressors (e.g., adverse childhood experiences) and recent stress exposure (e.g., recent life events) may contribute to the onset of depressive symptoms. However, their combined predictive effect on depression remains unclear. Using data from 2440 Chinese college students, the present study employed nine machine learning algorithms to evaluate the joint predictive roles of childhood adversity and recent stressors and to identify the most influential predictors through interpretable analyses. Results indicated that models incorporating both types of stressors achieved moderate predictive performance for depressive symptoms (R2 = 0.192-0.280; RMSE = 3.903-4.134). Key predictors included the childhood experience of being 'frequently bullied', academic-related recent stressors (e.g., 'disliking school', 'academic pressure from family', 'heavy workload', 'exam frustration', 'pressure regarding further education'), difficulties in life adjustment (e.g., 'noticeable changes in daily routines'), and interpersonal challenges (e.g., 'romantic relationship problems'). These findings highlight the importance of considering stressors from different developmental stages and offer empirical insights for the early identification and intervention of depression in college students.