Sacco and Jeong published a new article in the journal Addictive Behaviors.
Paul Sacco and Jihyeong Jeong have published their manuscript entitled, "Assessing the risk of problem gambling among lottery loyalty program members: A machine learning approach" in the journal, Addictive Behaviors. The authors found that rates of problem gambling are relatively high (14%) among lottery ticket players who participate in loyalty programs. Using a form of machine learning called the random forests algorithm, detection of problem was fairly accurate, but the model had low sensitivity meaning it struggled to detect those with problem gambling. Demographic factors such as income, education, and age as well as participation in other forms of gambling (e.g., casino gambling) were the most important predictors of problem gambling. With further refinement, predictive modeling may be combined with targeted prevention to intervene with indviduals who have gambling problems.
Sacco, P., & Jeong, J. (2025). Assessing the risk of problem gambling among lottery loyalty program members: A machine learning approach. Addictive Behaviors, 108372. https://doi.org/10.1016/j.addbeh.2025.108372