Yiliang Xin, Yan Wang, Xiyan Zhang, Peixuan Li, Wenyi Yang, Bosheng Wang, Jie Yang
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The study focused on prevalent mental health disorders and associated risk factors.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>The study revealed significant mental health disparities: depression (14.9%), anxiety (25.5%), and stress (10.9%) prevalences showed clear gender and regional gradients. Females exhibited higher rates across all conditions (p < 0.05), and urban areas had elevated risks compared to suburban regions. Mental health deterioration escalated with educational stages (e.g., depression from 9.2% in primary to 21.2% in senior high; χ²<sub>trend</sub> = 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 identified bullying duration, age, and drinking history as top risk factors across both Gain and SHAP methods, while SHAP values additionally emphasized modifiable lifestyle factors (e.g., breakfast frequency) and demographic variables (e.g., gender).</p><p><strong>Conclusions: </strong>This study identifies bullying, age, and alcohol consumption history as key mental health risk factors among Jiangsu's children and adolescents. These findings emphasize the need for school-based anti-bullying programs, age-specific mental health counseling, and healthy lifestyle education (including alcohol refusal). Lifestyle behaviors like daily breakfast intake should be integrated into dietary interventions for mental health promotion. Urban-rural and gender disparities necessitate targeted support for urban adolescent females, while educational stage differences highlight the criticality of early prevention.</p>","PeriodicalId":9934,"journal":{"name":"Child and Adolescent Psychiatry and Mental Health","volume":"19 1","pages":"100"},"PeriodicalIF":4.6000,"publicationDate":"2025-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12400694/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine learning-based analysis and prediction of factors influencing mental health among children and adolescents in Jiangsu Province.\",\"authors\":\"Yiliang Xin, Yan Wang, Xiyan Zhang, Peixuan Li, Wenyi Yang, Bosheng Wang, Jie Yang\",\"doi\":\"10.1186/s13034-025-00959-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>The study revealed significant mental health disparities: depression (14.9%), anxiety (25.5%), and stress (10.9%) prevalences showed clear gender and regional gradients. Females exhibited higher rates across all conditions (p < 0.05), and urban areas had elevated risks compared to suburban regions. Mental health deterioration escalated with educational stages (e.g., depression from 9.2% in primary to 21.2% in senior high; χ²<sub>trend</sub> = 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. 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Urban-rural and gender disparities necessitate targeted support for urban adolescent females, while educational stage differences highlight the criticality of early prevention.</p>\",\"PeriodicalId\":9934,\"journal\":{\"name\":\"Child and Adolescent Psychiatry and Mental Health\",\"volume\":\"19 1\",\"pages\":\"100\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2025-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12400694/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Child and Adolescent Psychiatry and Mental Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13034-025-00959-5\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PEDIATRICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Child and Adolescent Psychiatry and Mental Health","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13034-025-00959-5","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PEDIATRICS","Score":null,"Total":0}
引用次数: 0
摘要
背景:本研究采用标准化心理量表对江苏省儿童青少年的抑郁、焦虑和压力症状进行分析,调查其心理健康现状。利用机器学习模型识别关键影响变量并预测心理健康结果,旨在为这一人群建立快速的心理健康评估框架。目的:采用横断面随机整群抽样的方法,对江苏省98个县(市/区)的141725名学生进行调查,其中小学47,502名,初中47,274名,中职11,619名,高中35,330名。这项研究的重点是流行的精神健康障碍和相关的风险因素。方法:以抑郁、焦虑和压力得分为因变量,以57个社会人口统计学和行为因素为自变量。使用R软件实现了五个监督机器学习模型(决策树、朴素贝叶斯、随机森林、k近邻(KNN)和XGBoost)。采用正确率、精密度、召回率、F1评分和ROC曲线下面积(Area Under ROC Curve, AUC)评价模型的性能。进行特征重要性分析以确定关键预测因子。结果:心理健康存在显著差异:抑郁(14.9%)、焦虑(25.5%)和压力(10.9%)的患病率存在明显的性别和地区差异。结论:本研究确定欺凌、年龄和饮酒史是江苏儿童和青少年心理健康的主要危险因素。这些发现强调了以学校为基础的反欺凌项目、针对年龄的心理健康咨询和健康生活方式教育(包括拒绝饮酒)的必要性。日常早餐摄入等生活方式行为应纳入促进心理健康的饮食干预措施。城乡和性别差异需要对城市女性青少年提供有针对性的支持,而教育阶段的差异则突出了早期预防的重要性。
Machine learning-based analysis and prediction of factors influencing mental health among children and adolescents in Jiangsu Province.
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: The study revealed significant mental health disparities: depression (14.9%), anxiety (25.5%), and stress (10.9%) prevalences showed clear gender and regional gradients. Females exhibited higher rates across all conditions (p < 0.05), and urban areas had elevated risks compared to suburban regions. Mental health deterioration escalated with educational stages (e.g., depression from 9.2% in primary to 21.2% in senior high; χ²trend = 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 identified bullying duration, age, and drinking history as top risk factors across both Gain and SHAP methods, while SHAP values additionally emphasized modifiable lifestyle factors (e.g., breakfast frequency) and demographic variables (e.g., gender).
Conclusions: This study identifies bullying, age, and alcohol consumption history as key mental health risk factors among Jiangsu's children and adolescents. These findings emphasize the need for school-based anti-bullying programs, age-specific mental health counseling, and healthy lifestyle education (including alcohol refusal). Lifestyle behaviors like daily breakfast intake should be integrated into dietary interventions for mental health promotion. Urban-rural and gender disparities necessitate targeted support for urban adolescent females, while educational stage differences highlight the criticality of early prevention.
期刊介绍:
Child and Adolescent Psychiatry and Mental Health, the official journal of the International Association for Child and Adolescent Psychiatry and Allied Professions, is an open access, online journal that provides an international platform for rapid and comprehensive scientific communication on child and adolescent mental health across different cultural backgrounds. CAPMH serves as a scientifically rigorous and broadly open forum for both interdisciplinary and cross-cultural exchange of research information, involving psychiatrists, paediatricians, psychologists, neuroscientists, and allied disciplines. The journal focusses on improving the knowledge base for the diagnosis, prognosis and treatment of mental health conditions in children and adolescents, and aims to integrate basic science, clinical research and the practical implementation of research findings. In addition, aspects which are still underrepresented in the traditional journals such as neurobiology and neuropsychology of psychiatric disorders in childhood and adolescence are considered.