Moreover, problem gamblers showed a distinct pattern with respect to their gambling based on the player tracking data. The random forest model predicted self-reported problem gambling better than gradient boost. Random forest and gradient boost machine algorithms were trained to predict self-reported problem gambling based on the independent variables (e.g., wagering, depositing, gambling frequency). More specifically, the authors were given access to the raw data of 1,287 players from a European online gambling casino who answered questions on the Problem Gambling Severity Index (PGSI) between September 2021 and February 2022. In order to fulfil the aim, the study analyzed data from a sample of real-world online casino players and matched their self-report (subjective) responses concerning problem gambling with the participants’ actual (objective) gambling behavior. The present study’s main aim was to identify the most significant behavioral patterns which predict self-reported problem gambling. Problem gambling screens are one method for the collection of the necessary input data to train AI algorithms. AI algorithms require a training dataset to learn the patterns of a prespecified group. In recent years researchers have emphasized the importance of artificial intelligence (AI) algorithms as a tool to detect problem gambling online.
