Machine learning-based analysis and prediction of factors influencing mental health among children and adolescents in Jiangsu Province

作者：Yiliang Xin, Yan Wang, Xiyan Zhang, Peixuan Li, Wen‐Yi Yang, Bosheng Wang, Jie Yang · 发表于：Child and Adolescent Psychiatry and Mental Health · 年份：2025 · DOI：10.1186/s13034-025-00959-5 · 被引用次数：9 · 研究领域：Bullying, Victimization, and Aggression、Mental Health via Writing、COVID-19 and Mental Health

BACKGROUND: This study investigates the current mental health status among children and adolescents in Jiangsu Province by analyzing symptoms of depression, anxiety, and stress using standardized psychological scales. Machine learning models were utilized to identify key influencing variables and predict mental health outcomes, aiming to establish a rapid psychological well-being assessment framework for this population. OBJECTIVE: A cross-sectional survey was conducted via random cluster sampling across 98 counties (cities/districts) in Jiangsu Province, enrolling 141,725 students (47,502 primary, 47,274 junior high, 11,619 vocational high school students, and 35,330 senior high ). The study focused on prevalent mental health disorders and associated risk factors. METHODS: Depression, anxiety, and stress scores served as dependent variables, with 57 socio-demographic and behavioral factors as independent variables. Five supervised machine learning models (Decision Tree, Naive Bayes, Random Forest, K-Nearest Neighbors (KNN), and XGBoost) were implemented using R software. Model performance was evaluated using accuracy, precision, recall, F1 Score and Area Under the ROC Curve (AUC). Feature importance analysis was conducted to identify key predictors. RESULTS: = 2274.55, p < 0.05). The XGBoost model demonstrated optimal predictive performance (AUC: depression = 0.799, anxiety = 0.770, stress = 0.762), outperforming other models. Feature importance analysis consistently identif...