[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124211-en":3,"doc-seo-124211-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124211,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Application of machine learning in predicting adolescent Internet behavioral addiction","Objective: identify risk factors for adolescents’ Internet addiction behavior and build a prediction model using machine learning algorithms. Methods: 4461 high school students in Chongqing were sampled via stratified cluster sampling and assessed with questionnaires, then grouped into Internet addiction and non-addiction groups. Independent risk factors were tested using logistic regression, while six machine-learning methods were trained, compared by confusion matrices, and optimized by indicator performance. Results: the group differed by multiple demographic and psychological variables, and extreme gradient boosting achieved the best predictive performance.","TYPE Original Research PUBLISHED 01 April 2025  \nDOI 10.3389/fpsyt.2024.1521051  \nOPEN ACCESS  \nEDITED BY  \nYibo Wu,  \nPeking University, China  \nREVIEWED BY  \nMark Cheuk-man Tsang, Tung Wah College, China Mahmoud Mamdouh Elhabiby, Ain Shams University, Egypt Cemal Onur Noyan,  \nÜsküdar University, Türkiye  \n*CORRESPONDENCE  \nLi Kuang  \n [KuangLi0309@126.com](KuangLi0309@126.com)  \nRECEIVED 01 November 2024  \nACCEPTED 30 December 2024  \nPUBLISHED 01 April 2025  \nCITATION  \nGan Y, Kuang L, Xu X-M, Ai M, He J-L, Wang W, Hong S, Chen Jm, Cao J and Zhang Q (2025) Application of machine learning in predicting adolescent Internet behavioral addiction.  \nFront. Psychiatry 15:1521051 .  \ndoi: 10.3389/fpsyt.2024.1521051  \nCOPYRIGHT  \n© 2025 Gan, Kuang, Xu, Ai, He, Wang, Hong, Chen, Cao and Zhang. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nApplication of machine learning in predicting adolescent Internet behavioral addiction  \nYao Gan 1, Li Kuang 1*, Xiao-Ming Xu1, Ming Ai1, Jing-Lan He 1, Wo Wang 2, Su Hong 1, Jian mei Chen 1, Jun Cao 1 and Qi Zhang 1  \n1 Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University,  \nChongqing, China, 2 Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China  \nObjective: To explore the risk factors affecting adolescents ’ Internet addiction behavior and build a prediction model for adolescents ’ Internet addiction behavior based on machine learning algorithms.  \nMethods: A total of 4461 high school students in Chongqing were selected using stratified cluster sampling, and questionnaires were administered. Based on the presence of Internet addiction behavior, students were categorized into an Internet addiction group (n=1210) and a non-Internet addiction group (n=3115) . Gender, age, residence type, and other data were compared between the groups, and independent risk factors for adolescent Internet addiction were analyzed using a logistic regression model. Six methods—multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting—were used to construct the model. The model ’ s indicators under each algorithm were compared, evaluated with a confusion matrix, and the optimal model was selected.  \nResult: The proportion of male adolescents, urban household registration, and scores on the family function, planning, action, and cognitive subscales, along with psychoticism, introversion-extroversion, neuroticism, somatization, obsessive-compulsiveness, interpersonal sensitivity, depression, anxiety, hostility, paranoia, and psychosis, were significantly higher in the Internet addiction group than in the non-Internet addiction group (P \u003C 0 . 05) . No significant differences were found in age or only-child status (P > 0 . 05) . Statistically significant variables were analyzed using a logistic regression model, revealing that gender, household registration type, and scores on planning, action, introversion-extroversion, psychoticism, neuroticism, cognitive, obsessive-compulsive, depression, and hostility scales are independent risk factors for adolescent Internet addiction. The area under the curve (AUC) for multi-level perceptron, random forest, K-nearest neighbor, support vector machine, logistic regression, and extreme gradient boosting models were 0.843, 0.817, 0.778, 0.846, 0.847, and 0.836, respectively, with extreme gradient boosting showing the best predictive performance among these models.  \nFrontiers in Psychiatry 01 [frontiersin.org](frontiersin.org)  \nConclusi","cbCaioypYnmiewju","https://ap.wps.com/l/cbCaioypYnmiewju","pdf",861921,1,"English","en",105,"# Objective\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What was the main objective of the study?\",\"answer\":\"To explore risk factors for adolescents’ Internet addiction behavior and develop a prediction model using machine learning algorithms.\"},{\"question\":\"How were participants selected and grouped?\",\"answer\":\"A total of 4461 high school students in Chongqing were selected using stratified cluster sampling, and students were categorized into Internet addiction and non-Internet addiction groups based on the presence of Internet addiction behavior.\"},{\"question\":\"Which model showed the best predictive performance?\",\"answer\":\"Extreme gradient boosting showed the best performance, with the highest reported AUC among the compared machine-learning methods.\"}]","Application of machine learning in predicting adolescent Internet behavioral addiction | 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