{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T23:23:45Z","timestamp":1780529025966,"version":"3.54.1"},"reference-count":57,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T00:00:00Z","timestamp":1753056000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>The internet is one of the essential tools today, and its impact is particularly felt among university students. Internet addiction (IA) has become a serious public health issue worldwide. This multi-class classification study aimed to identify the potential predictors of IA by four severity levels among university students in Bangladesh. We used cross-sectional survey data from 424 university students from different universities in Bangladesh. Data was collected using a self-reported questionnaire, along with an IA test to assess addiction levels. We identified the important features related to IA using the Boruta algorithm. Predictions were made using different machine learning (ML) (decision tree (DT), random forest (RF), support vector machines (SVMs), and logistic regression (LR)) models. Their performance was assessed using confusion matrix parameters, receiver operating characteristics (ROC) curves, and k-fold cross-validation techniques for multi-class classification problems. The prevalence of severe IA was 3.77% among the participating university students in Bangladesh from July 15 to July 22, 2024. University students\u2019 backgrounds, depression, anxiety, stress, participation in physical activity, misbehaving with family members, memory loss symptoms, and being COVID-19-positive were selected as significant features for predicting IA. Overall, the RF (accuracy\u2009=\u20090.531, sensitivity\u2009=\u20090.200, specificity\u2009=\u20090.986, precision\u2009=\u20091.00, k-fold accuracy\u2009=\u20090.4858, micro-average area under curve (AUC) = 0.7798) more accurately predicted IA compared to other ML techniques. The ML framework for multi-class classification study can reveal significant risk factors and predict this behavioral addiction more precisely. It can help policymakers, stakeholders, and families better understand the situation and prevent this severe crisis by improving policy-making strategies, promoting mental health, and establishing effective university counseling services. Therefore, raising awareness among the younger generation and their parents about the predictors of IA is important.<\/jats:p>","DOI":"10.1371\/journal.pone.0325855","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T17:34:51Z","timestamp":1753119291000},"page":"e0325855","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["An illustration of multi-class roc analysis for predicting internet addiction among university students"],"prefix":"10.1371","volume":"20","author":[{"given":"Nishat Tasnim","family":"Thity","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3291-7836","authenticated-orcid":true,"given":"Atikur","family":"Rahman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adisha","family":"Dulmini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mst. Nilufar","family":"Yasmin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0751-7104","authenticated-orcid":true,"given":"Rumana","family":"Rois","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"pone.0325855.ref001","first-page":"279","article-title":"Internet addiction among students: prevalence and psychological problems in Japan","volume":"49","author":"T Sato","year":"2006","journal-title":"Japan Med Assoc J"},{"issue":"3","key":"pone.0325855.ref002","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1089\/cpb.1998.1.237","article-title":"Internet addiction: the emergence of a new clinical disorder","volume":"1","author":"KS Young","year":"1998","journal-title":"CyberPsychol Behav"},{"issue":"1","key":"pone.0325855.ref003","doi-asserted-by":"crossref","first-page":"61","DOI":"10.4103\/ipj.ipj_61_17","article-title":"A study on internet addiction and its relation to psychopathology and self-esteem among college students","volume":"27","author":"M Kumar","year":"2018","journal-title":"Ind Psychiatry J"},{"key":"pone.0325855.ref004","doi-asserted-by":"crossref","first-page":"9910351","DOI":"10.1136\/sbmj.9910351","article-title":"Internet addiction: evaluation and treatment","volume":"319","author":"KS Young","year":"1999","journal-title":"BMJ"},{"issue":"4","key":"pone.0325855.ref005","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1177\/0002764204270278","article-title":"Internet addiction: a new clinical phenomenon and its consequences","volume":"48","author":"KS Young","year":"2004","journal-title":"Am Behav Sci"},{"key":"pone.0325855.ref006","article-title":"Behavioural addiction: an issue for everybody?","author":"M Griffiths","year":"1996","journal-title":"Empl Councel Today"},{"issue":"4","key":"pone.0325855.ref007","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/14659890500114359","article-title":"A \u2018components\u2019 model of addiction within a biopsychosocial framework","volume":"10","author":"M Griffiths","year":"2005","journal-title":"J Subst Use"},{"key":"pone.0325855.ref008","author":"Bangladesh Telecommunication Regulatory Commission (BTRC)"},{"key":"pone.0325855.ref009","author":"O Egger","year":"1996"},{"issue":"6","key":"pone.0325855.ref010","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1093\/heapro\/daab007","article-title":"Internet use and health in higher education students: a scoping review","volume":"36","author":"H Rouvinen","year":"2021","journal-title":"Health Promot Int"},{"issue":"2","key":"pone.0325855.ref011","doi-asserted-by":"crossref","first-page":"44","DOI":"10.5005\/jp-journals-10067-0037","article-title":"Study of internet use characteristics, perceived stress, and internet addiction among first-year medical students of Jamnagar, Gujarat, India","volume":"13","author":"VK Patel","year":"2019","journal-title":"Indian J Priv Psychiatry"},{"issue":"1","key":"pone.0325855.ref012","first-page":"138","article-title":"Internet addiction and depression, anxiety and stress","volume":"3","author":"A Akin","year":"2011","journal-title":"Inter Online J Edu Sci"},{"key":"pone.0325855.ref013","doi-asserted-by":"crossref","unstructured":"Derbyshire K, Lust K, Schreiber L, Odlaug B, Christenson G, Golden D, et al. 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