Using machine-learning algorithms to predict self-reported problem gambling among a sample of online gamblers

Auer, M and Griffiths, MD ORCID: https://orcid.org/0000-0001-8880-6524, 2026. Using machine-learning algorithms to predict self-reported problem gambling among a sample of online gamblers. International Journal of Mental Health and Addiction. ISSN 1557-1874

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Abstract

Studies suggest that algorithms can effectively be used to predict self-reported problem gambling using player tracking data. The present study analyzed a sample of real-world online gamblers (N = 1,611) who engaged in lottery playing, casino gambling, bingo playing, and sports betting. The data also comprised each player’s actual gambling activity, as well as age and gender, in the 30 days prior to answering the Problem Gambling Severity Index (PGSI). Players who engaged in at least one lottery game 30 days prior to answering the PGSI were less likely to be problem gamblers compared to players who did not play lottery games. For all other game-categories the relationship was reversed. The results also indicated that specific behavioral tracking features—such as the average number of monetary deposits per session, total amount of money bet per day, session length, and casino gambling involvement—were among the most significant predictors of self-reported problem gambling. When evaluating different machine algorithms, logistic regression and random forest emerged as the most effective in predicting self-reported problem gambling. The present study is among the few which predicts self-reported problem gambling using a sample of online lottery players, casino gamblers, bingo players and sports bettors, and provides further empirical evidence supporting the use of machine learning models to identify self-reported problem gamblers based on player tracking data. These findings can inform responsible gambling strategies by enabling operators to identify and intervene before gambling-related problems escalate.

Item Type:	Journal article
Publication Title:	International Journal of Mental Health and Addiction
Creators:	Auer, M. and Griffiths, M.D.
Publisher:	Springer Science and Business Media LLC
Date:	16 January 2026
ISSN:	1557-1874
Identifiers:	Number Type 10.1007/s11469-025-01602-2 DOI 2560512 Other
Rights:	© The Author(s) 2026 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Social Sciences
Record created by:	Jeremy Silvester
Date Added:	23 Jan 2026 09:51
Last Modified:	23 Jan 2026 09:51
URI:	https://irep.ntu.ac.uk/id/eprint/55098

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