Andersson Sam, Carlbring Per, Lyon Keenan, Bermell Måns, Lindner Philip
1Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm Health Care Services, Region Stockholm, Stockholm, Sweden.
2Department of Psychology, Stockholm University, Stockholm, Sweden.
J Behav Addict. 2025 Feb 27;14(1):490-500. doi: 10.1556/2006.2025.00013. Print 2025 Mar 28.
The digitalization of gambling provides unprecedented opportunities for early identification of problem gambling, a well-recognized public health issue. This study aimed to advance current practices by employing advanced machine learning techniques to predict problem gambling behaviors and assess the temporal stability of these predictions.
We analyzed player account data from a major Swedish online gambling provider, covering a 4.5-year period. Feature engineering was applied to capture gambling behavior dynamics. We trained machine learning models, XGBoost, to classify players into low-risk and higher-risk categories. Temporal stability was evaluated by progressively truncating the training dataset at various time points (30, 60, and 90 days) and assessing model performance across truncations.
The models demonstrated considerable predictive accuracy and temporal stability. Key features such as loss-chasing behavior and net balance trend consistently contributed to accurate predictions across all truncation periods. The model's performance evaluated on a separate holdout set, measured by metrics like F1 score and ROC AUC, remained robust, with no significant decline observed even with reduced data, supporting the feasibility of early and reliable detection.
These findings indicate that machine learning can reliably predict problem gambling behaviors over time, offering a scalable alternative to traditional methods. Temporal stability highlights their potential for real-time application in gambling operators' Duty of Care. Consequently, advanced techniques could strengthen early identification and intervention strategies, potentially improving public health outcomes by preventing the escalation of harmful behaviors.
赌博数字化为早期识别问题赌博提供了前所未有的机遇，问题赌博是一个公认的公共卫生问题。本研究旨在通过运用先进的机器学习技术来预测问题赌博行为并评估这些预测的时间稳定性，从而改进当前的做法。
我们分析了一家瑞典主要在线赌博提供商的玩家账户数据，涵盖4.5年的时间段。应用特征工程来捕捉赌博行为动态。我们训练了机器学习模型XGBoost，将玩家分类为低风险和高风险类别。通过在不同时间点（30天、60天和90天）逐步截断训练数据集并评估各截断情况下的模型性能来评估时间稳定性。
模型显示出相当高的预测准确性和时间稳定性。诸如追损行为和净余额趋势等关键特征在所有截断期内都始终有助于准确预测。在一个单独的保留集上评估的模型性能，以F1分数和ROC AUC等指标衡量，保持稳健，即使数据减少也未观察到显著下降，支持了早期可靠检测的可行性。
这些发现表明，机器学习能够随时间可靠地预测问题赌博行为，为传统方法提供了一种可扩展的替代方案。时间稳定性凸显了它们在赌博运营商的照护职责中进行实时应用的潜力。因此，先进技术可以加强早期识别和干预策略，有可能通过防止有害行为升级来改善公共卫生结果。
