DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wonju Seo | - |
dc.contributor.author | Namho Kim | - |
dc.contributor.author | Sang-Kyu Lee | - |
dc.contributor.author | Sung-Min Park | - |
dc.date.accessioned | 2021-06-01T04:56:44Z | - |
dc.date.available | 2021-06-01T04:56:44Z | - |
dc.date.created | 2020-08-28 | - |
dc.date.issued | 2020-09 | - |
dc.identifier.issn | 2062-5871 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/105567 | - |
dc.description.abstract | Background and aims: Problem gambling among adolescents has recently attracted attention because of easy access to gambling in online environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling. Methods: 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. Results: 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 >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. Discussion: The results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling. Conclusion: 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. | - |
dc.language | English | - |
dc.publisher | AKADEMIAI KIADO ZRT | - |
dc.relation.isPartOf | JOURNAL OF BEHAVIORAL ADDICTIONS | - |
dc.title | Machine learning-based analysis of adolescent gambling factors | - |
dc.type | Article | - |
dc.identifier.doi | 10.1556/2006.2020.00063 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | JOURNAL OF BEHAVIORAL ADDICTIONS, v.9, no.3, pp.734 - 743 | - |
dc.identifier.wosid | 000577516600018 | - |
dc.citation.endPage | 743 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 734 | - |
dc.citation.title | JOURNAL OF BEHAVIORAL ADDICTIONS | - |
dc.citation.volume | 9 | - |
dc.contributor.affiliatedAuthor | Wonju Seo | - |
dc.contributor.affiliatedAuthor | Namho Kim | - |
dc.contributor.affiliatedAuthor | Sung-Min Park | - |
dc.identifier.scopusid | 2-s2.0-85092943958 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | Adolescents | - |
dc.subject.keywordPlus | Problem gambling | - |
dc.subject.keywordPlus | Machine learning-based analysis method | - |
dc.subject.keywordPlus | Feature engineering | - |
dc.subject.keywordAuthor | adolescents | - |
dc.subject.keywordAuthor | problem gambling | - |
dc.subject.keywordAuthor | machine learning-based analysis method | - |
dc.subject.keywordAuthor | feature engineering | - |
dc.relation.journalWebOfScienceCategory | Psychiatry | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Psychiatry | - |
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