AI-Enabled Digital Phenotyping for Personalized Risk Stratification in Internet Gaming Disorder: A Privacy-Preserving Simulation Study

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Abstract
Background/Objectives: Assessment of Internet Gaming Disorder (IGD) relies largely on retrospective self-report and clinical interviews, which may be affected by recall bias, social desirability bias, and limited sensitivity to within-person behavioral change. This study evaluated an AI-enabled, privacy-preserving digital phenotyping framework for personalized IGD risk stratification under controlled simulation assumptions. Methods: A reproducible synthetic dataset of 1,000 virtual user profiles was generated; 20% were assigned to an elevated-risk class, and 5% balanced stochastic label noise was introduced to approximate imperfect ground truth. Four aggregated telemetry features were modeled: average session duration, sessions per week, Late-Night Index, and application-switching rate. Random Forest, Logistic Regression, and Gradient Boosting classifiers were evaluated against playtime-only baselines using a stratified 80:20 train–test split. Results: In the primary Random Forest model, accuracy was 0.880, balanced accuracy was 0.850, sensitivity was 0.800, specificity was 0.900, area under the receiver operating characteristic curve (ROC-AUC) was 0.909, area under the precision–recall curve (PR-AUC) was 0.779, and the Brier score was 0.089. All-feature models substantially outperformed playtime-only baselines. Feature-importance analyses recovered the known signal hierarchy encoded in the synthetic data-generating process, with application-switching rate and Late-Night Index showing the largest Gini-based and permutation-importance values. Performance degraded progressively as label noise increased from 0% to 20%. Conclusions: The framework demonstrates the methodological feasibility of transforming aggregated, privacy-preserving behavioral telemetry into interpretable simulated risk signals for IGD. The findings are hypothesis-generating and do not establish clinical validity or diagnostic performance. Longitudinal validation in clinically characterized cohorts using validated psychometric instruments and person-level calibration is required before practical deployment.

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