Predicting Mental Health Among Adolescents with Risk Behaviors Based on Machine Learning
DOI:
https://doi.org/10.12775/PPS.2025.28.67521Keywords
risk behaviors, mental health, adolescents, machine learning, gender differencesAbstract
Objectives
While mental health is known to predict risk behaviors, less is understood about how specific risk behaviors contribute to mental health outcomes, particularly across genders. This study used machine learning to examine the predictive relationships between various risk behaviors and adolescent mental health, and to explore gender differences in these patterns.
Methods
We analyzed data from the nationally representative Chinese “Database of Youth Health,” including 8,670 high school students surveyed in 2020. A gradient-boosted decision tree model (XGBoost) was used to predict mental health, measured by the Symptom Checklist-90 (SCL-90), based on 22 risk behaviors. SHAP (Shapley Additive ExPlanations) values were calculated to interpret individual feature contributions.
Results
The model showed good performance (RMSE = 0.49, MAE = 0.35). Frequent dizziness during sports, lack of seat belt use, and alcohol consumption were identified as significant risk factors. Gender differences emerged: earlier age of first smoking was more strongly associated with poorer mental health among girls, while exercise frequency was a stronger protective factor for boys.
Conclusion
These findings underscore the need for gender-sensitive mental health interventions that address both physical and behavioral risk factors, and demonstrate the utility of machine learning in identifying nuanced predictors of adolescent mental health.
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