Assessing the risk of problem gambling among lottery loyalty program members: A machine learning approach.

BACKGROUND AND AIMS: Lottery gambling is a relatively benign form of gambling. Nonetheless, individuals with gambling problems may engage in lottery play and/or play the lottery exclusively. Lottery loyalty programs have data that could be used to screen for problem gambling, as they collect information on demographics and ticket purchases from players who sign up to receive incentives. The current study evaluates the feasibility of machine learning to identify individuals who have gambling problems using data collected from a state lottery loyalty program.

METHODS: Data from ticket uploads was merged with an online survey sent to loyalty program participants (N = 5903). The Problem Gambling Severity Index (PGSI) was used to screen for problem gambling, with a five or greater denoting problem gambling (n = 809; 14%). Other survey items queried frequency of other gambling (e.g., casino slot machine) as well as amounts spent. Random forests analysis, a predictive modeling technique, was used to predict individuals who have gambling problems.

DISCUSSION AND CONCLUSIONS: Problem gambling was more common among loyalty program players than typical in population samples. The random forest algorithm performed fairly well overall, but sensitivity was poor, indicating that the model did not identify individuals with problem gambling effectively. Lottery loyalty programs may be a promising setting for screening and secondary prevention efforts because of relatively high prevalence of problem gambling, but random forests may not be the best approach for detecting those at risk.