Using machine learning to retrospectively predict self‐reported gambling problems in Quebec

Abstract

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.