{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T05:39:14Z","timestamp":1766986754352,"version":"3.48.0"},"reference-count":64,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Public Health Challenges"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Internet addiction (IA) among adolescents is growing worldwide. Online temptation is particularly strong for adolescents due to rapid physical and cognitive development. IA may impair their mental, emotional, social, and physical health. Few traditional studies were conducted in Bangladesh. Thus, this study aimed to identify adolescents\u2019 IA risk factors using advanced machine learning (ML).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A total of 385 individuals were convenience sampled and surveyed using the Patient Health Questionnaire\u20109 (PHQ\u20109), the UCLA Loneliness Scale (UCLA\u20103), and Young's IA Test (IAT\u201020) to measure the prevalence of depression, loneliness, and IA. Boruta found IA prevalence classifying factors. We evaluated decision tree (DT), support vector machine (SVM), logistic regression (LR), and random forest (RF) classification models using confusion matrix, receiver operating characteristic (ROC) curves, and\n                      <jats:italic>k<\/jats:italic>\n                      \u2010fold cross\u2010validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 385 respondents, one\u2010third (30.1%) reported IA. Participants\u2019 fathers\u2019 education, favorite activity, loneliness, smoking status, depression, and internet use time were selected as important features classifying IA. The performance was tested on the basis of five different classification techniques overall: the SVM linear kernel model (accuracy\u00a0=\u00a00.819, specificity\u00a0=\u00a00.869, sensitivity\u00a0=\u00a00.687, precision\u00a0=\u00a00.666, area under the ROC curve [AUC]\u00a0=\u00a00.890,\n                      <jats:italic>k<\/jats:italic>\n                      \u2010fold accuracy\u00a0=\u00a00.801) performed better and authentically classified IA.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Raising awareness among adolescents and their parents is crucial because IA is frequent. The ML framework can identify significant prognostic indicators and classify this IA problem more accurately, helping policymakers, stakeholders, and families understand and prevent this crisis by improving policy\u2010making strategies and counseling services.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/puh2.70165","type":"journal-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T15:23:06Z","timestamp":1763133786000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classifying Internet Addiction Using Machine Learning Approach: A Study Among Adolescents in Bangladesh"],"prefix":"10.1002","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7171-0737","authenticated-orcid":false,"given":"Akher","family":"Ali","sequence":"first","affiliation":[{"name":"Department of Statistics and Data Science Jahangirnagar University, Savar  Dhaka Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0010-3102","authenticated-orcid":false,"given":"Md. Sahadat","family":"Hosain","sequence":"additional","affiliation":[{"name":"Department of Development Studies University of Chittagong  Chittagong Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7752-1953","authenticated-orcid":false,"given":"Md Abu Bakkar","family":"Siddik","sequence":"additional","affiliation":[{"name":"School of the Environment Nanjing University  Nanjing China"},{"name":"Department of Development Studies Daffodil International University, Savar  Dhaka Bangladesh"},{"name":"The Center for Social Policy and Justice  Dhaka Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3263-4636","authenticated-orcid":false,"given":"Mahedi","family":"Hasan","sequence":"additional","affiliation":[{"name":"The Center for Social Policy and Justice  Dhaka Bangladesh"},{"name":"College of Media and Communication Texas Tech University  Lubbock Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md. Ahashan","family":"Habib","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science Jahangirnagar University, Savar  Dhaka Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Alamgir","family":"Kabir","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science Jahangirnagar University, Savar  Dhaka Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Mizanur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Business Administration Manarat International University  Dhaka Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7505-9881","authenticated-orcid":false,"given":"Peal Ahamed","family":"Shanto","sequence":"additional","affiliation":[{"name":"Department of Economics Texas Tech University  Lubbock Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5959-9669","authenticated-orcid":false,"given":"Nafiul","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Statistics Shahjalal University of Science and Technology  Sylhet Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8194-6874","authenticated-orcid":false,"given":"Al","family":"Mahmud","sequence":"additional","affiliation":[{"name":"School of Dental Sciences Health Campus Universiti Sains Malaysia  Kelantan Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CHB.2013.11.019"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.CHB.2014.10.039"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.7334\/psicothema2012.274"},{"volume-title":"Internet Usage Worldwide\u2014Statistics & Facts","year":"2023","author":"Petrosyan A.","key":"e_1_2_12_5_1"},{"volume-title":"Internet Freedom and Access to Information: Where Are We?","year":"2023","author":"Shoma A.","key":"e_1_2_12_6_1"},{"volume-title":"Teens and Social Media: Key Findings From Pew Research Center Surveys","year":"2023","author":"Vogels E. 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