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We proposed a machine learning-based analysis method for predicting the degree of problem gambling.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Of the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling Problems), 5,045 students who had gambled in the past 3 months were included in this study. The Gambling Problem Severity Scale was used to provide the binary label information. After the random forest-based feature selection method, we trained four models: random forest (RF), support vector machine (SVM), extra trees (ETs), and ridge regression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The online gambling behavior in the past 3 months, experience of winning money or goods, and gambling of personal relationship were three factors exhibiting the high feature importance. All four models demonstrated an area under the curve (AUC) of &gt;0.7; ET showed the highest AUC (0.755), RF demonstrated the highest accuracy (71.8%), and SVM showed the highest F1 score (0.507) on a testing set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>The results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Machine learning models trained using important features showed moderate accuracy in a large-scale Korean adolescent dataset. These findings suggest that the method will help screen adolescents at risk of problem gambling. We believe that expandable machine learning-based approaches will become more powerful as more datasets are collected.<\/jats:p><\/jats:sec>","DOI":"10.1556\/2006.2020.00063","type":"journal-article","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T11:35:41Z","timestamp":1602156941000},"page":"734-743","update-policy":"https:\/\/doi.org\/10.1556\/policypage.crossmark.crossref.1","source":"Crossref","is-referenced-by-count":23,"title":["Machine learning-based analysis of adolescent gambling factors"],"prefix":"10.1556","volume":"9","author":[{"given":"Wonju","family":"Seo","sequence":"first","affiliation":[{"name":"1Department of Creative IT Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Nam-gu, Pohang, 37673, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Namho","family":"Kim","sequence":"additional","affiliation":[{"name":"1Department of Creative IT Engineering, Pohang University of Science and Technology, 77 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