Explainable Machine Learning Prediction of Internet Addiction Among Chinese Primary and Middle School Children and Adolescents: A Longitudinal Study Based on Positive Youth Development Data (2019-2022)

Jiahe Liu^1,2Lang Chen¹Yuxin Chen³Jingsong Luo³Kexin Yu⁴Linlin Fan⁴Chan Yong⁴Huiyu He⁴Simei Liao⁵Lihua Jiang^4,6*

• ¹School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Parkville, Victoria, Australia
• ²AIM for Health Lab, Faculty of IT, Monash University, Clayton, Australia
• ³The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong Region, China
• ⁴West China School of Public Health, Sichuan University, Chengdu, Sichuan Province, China
• ⁵School of Nursing, Capital Medical University, Beijing, Beijing Municipality, China
• ⁶Teaching & Research Section of General Practice Medical Center, West China Hospital of Sichuan University, Chengdu, China

Background: Internet Addiction (IA) has emerged as a critical concern, especially among school age children and adolescents, potentially stalling their physical and mental development. Our study aimed to examine the risk factors associated with IA among Chinese children and adolescents and leverage explainable machine learning (ML) algorithms to predict IA status at the time of assessment, based on Young’s Internet Addiction Test.Methods: The longitudinal data consisting of 8824 schoolchildren from the Chengdu Positive Child Development (CPCD) survey were analysed, where 33.3% of participants were identified with IA (Age: 10.97 ± 2.31, Male: 51.73%). IA was defined using Young’s Internet Addiction Test (IAT ≥ 40). Demographic variables such as age, gender, and grade level, along with key variables including scores of Cognitive Behavioural Competencies (CBC), Prosocial Attributes (PA), Positive Identity (PI), General Positive Youth Development Qualities (GPYDQ), Life Satisfaction (LS), Delinquent Behaviour (DB), Non-Suicidal Self-Injury (NSSI), Depression (DP), Anxiety (AX), Family Function Disorders (FF), Egocentrism (EG), Empathy (EP), Academic Intrinsic Value (IV), and Academic Utility Value (UV) were examined. Chi-square and Mann-Whitney U tests were employed to validate the significance of the mentioned predictors of IA. We applied six ML models: Extra Random Forest, XGBoost, Logistic Regression, Bernoulli Naïve Bayes, Multi-Layer Perceptron (MLP), and Transformer Encoder. Performance was evaluated via 10-fold cross-validation and held-out test sets across survey waves. Feature selection and SHapley Additive exPlanations (SHAP) analysis were utilised for model improvement and interpretability, respectively.Results: ExtraRFC achieved the best performance (Test AUC = 0.854, Accuracy = 0.798, F1 = 0.659), outperforming all other models across most metrics and external validations. Key predictors included grade level, delinquent behaviour, anxiety, family function, and depression scores. SHAP analysis revealed consistent and interpretable feature contributions across individuals.Conclusions: Depression, anxiety, and family dynamics as significant factors influencing IA in children. The Extra Random Forest model proves most effective in predicting IA, emphasising the importance of addressing these factors to promote healthy digital habits in children. This study presents an effective SHAP-based explainable ML framework for IA prediction in children and adolescents.