Using machine learning to retrospectively predict self‐reported gambling problems in Quebec.
Background and Aims: Participating in online gambling is associated with an increased risk for experiencing gambling‐related harms, driving calls for more effective, personalized harm prevention initiatives. Such initiatives depend on the development of models capable of detecting at‐risk online gam...
| Publicado en: | Addiction Vol. 118; no. 8; pp. 1569 - 1579 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164656268&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164656268 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09652140 AIO jtl: Addiction issn: 09652140 maglogo: Y pubinfo: dt: Aug2023 vid: 118 iid: 8 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 164656268 162532075 164656268 164656268 10.1111/add.16179 164656268 ppf: 1569 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Using machine learning to retrospectively predict self‐reported gambling problems in Quebec. aug: au: Murch, W. Spencer Kairouz, Sylvia Dauphinais, Sophie Picard, Elyse Costes, Jean‐Michel French, Martin affil: Department of Sociology and Anthropology, Concordia University, Montreal Quebec, , Canada sug: subj: Gambling Quebec Self Report Machine Learning Impulse Control Disorders Risk Factors Risk Assessment Quebec Human Adult Questionnaires Scales Confidence Intervals Exploratory Research ROC Curve Sensitivity and Specificity Funding Source Adult: 19-44 years ab: Background and Aims: Participating in online gambling is associated with an increased risk for experiencing gambling‐related harms, driving calls for more effective, personalized harm prevention initiatives. Such initiatives depend on the development of models capable of detecting at‐risk online gamblers. We aimed to determine whether machine learning algorithms can use site data to detect retrospectively at‐risk online gamblers indicated by the Problem Gambling Severity Index (PGSI). Design: Exploratory comparison of six prominent supervised machine learning methods (decision trees, random forests, K‐nearest neighbours, logistic regressions, artificial neural networks and support vector machines) to predict problem gambling risk levels reported on the PGSI. Setting: Lotoquebec.com (formerly espacejeux.com), an online gambling platform operated by Loto‐Québec (a provincial Crown Corporation) in Quebec, Canada. Participants: N = 9145 adults (18+) who completed the survey measure and placed at least one bet using real money on the site. Measurements Participants completed the PGSI, a self‐report questionnaire with validated cut‐offs denoting a moderate‐to‐high‐risk (PGSI 5+) or high‐risk (PGSI 8+) for experiencing past‐year gambling‐related problems. Participants agreed to release additional data about the preceding 12 months from their user accounts. Predictor variables (144) were derived from users' transactions, apparent betting behaviours, listed demographics and use of responsible gambling tools on the platform. Findings Our best classification models (random forests) for the PGSI 5+ and 8+ outcome variables accounted for 84.33% (95% CI = 82.24–86.41) and 82.52% (95% CI = 79.96–85.08) of the total area under their receiver operating characteristic curves, respectively. The most important factors in these models included the frequency and variability of participants' betting behaviour and repeat engagement on the site. Conclusions: Machine learning algorithms appear to be able to classify at‐risk online gamblers using data generated from their use of online gambling platforms. They may enable personalized harm prevention initiatives, but are constrained by trade‐offs between their sensitivity and precision. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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