Predicting Digital Addiction Patterns with Machine Learning for Personalised Mental Health Support

The exponential growth of social media has heightened concerns about digital addiction and its mental health consequences, particularly among younger populations. Existing digital health tools, including conversational agents and large language models, offer real-time support but often neglect the predictive value of structured behavioural data. This study introduces a machine learning framework to assess digital addiction risk using 3200 anonymised self-reports comprising screen time, social media engagement, sleep duration, and mental health indicators. Across multiple models, categorical boosting (CatBoost) achieves the highest performance (precision = 85.4%, receiver operating characteristic-area under the curve (ROC-AUC) = 0.93), outperforming extreme gradient boosting (XGBoost) and graph neural networks (GNN). A linear regression model provides interpretable correlations between behavioural variables and addiction risk. Structural equation modelling (SEM) reveals that anxiety and depression mediate the relationship between digital behaviours and addiction risk, offering causal insights into these pathways. Feature importance analysis identified excessive screen time, frequent social media checking, and reduced sleep as the most influential predictors. To translate findings into practice, K-means clustering generated behavioural risk profiles, enabling personalised, data-driven recommendations. While clinical validation remains a next step, this framework demonstrates how predictive modelling and clustering can inform scalable, non-invasive digital health interventions. By integrating machine learning with causal modelling and personalised intervention design, this study advances computational approaches to digital addiction and contributes to the broader discourse on artificial intelligence applications in mental health and social computing.