Building Machine Learning Predictive Models for Adolescent Internet Addiction: Key Findings on Self-Esteem and Resilience Interaction

Objective: Internet addiction (IA) is a significant mental health concern among adolescents. This study aimed to develop machine learning (ML)-based predictive models to identify and explain key risk factors for IA. Method: A total of 8176 junior high school students from Henan Province were surveyed from April to May 2023. The dataset was randomly divided into training and test sets in an 8:2 ratio. Four ML algorithms were used to predict IA, and feature importance was determined using SHapley Additive exPlanations (SHAP). The XGBoost model, which achieved the highest area under the curve (AUC), was selected for detailed analysis and individualized prediction explanations. Results: The five most important predictors of IA were negative life events, self-esteem, school connectedness, parent-adolescent cohesion, and psychological resilience. Importantly, an interaction effect was found between self-esteem and psychological resilience: as self-esteem increased, the influence of low resilience transitioned from being a risk factor to a protective factor against IA. Conclusion: This study demonstrates the power of ML models combined with SHAP for predicting IA and identifying its psychosocial determinants. The findings highlight the critical interplay of self-esteem and psychological resilience, offering valuable insights for clinicians and educators in addressing IA among adolescents.