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

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This summary is machine-generated.

Machine learning models can identify online gamblers at risk of harm using site data. This enables personalized prevention strategies by analyzing betting frequency, variability, and engagement.

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Area of Science:

  • Computational Social Science
  • Machine Learning in Behavioral Science
  • Online Gambling Research

Background:

  • Online gambling participation correlates with increased gambling-related harms.
  • Effective harm prevention requires early detection of at-risk individuals.
  • Machine learning offers potential for personalized intervention models.

Purpose of the Study:

  • To assess machine learning algorithms' ability to detect at-risk online gamblers.
  • To utilize site-generated data for retrospective identification of problem gambling.
  • To compare the efficacy of various supervised machine learning methods.

Main Methods:

  • Exploratory comparison of six supervised machine learning algorithms.
  • Prediction of problem gambling risk using the Problem Gambling Severity Index (PGSI).
  • Analysis of 144 predictor variables from user transaction and behavior data on Lotoquebec.com.

Main Results:

  • Random forest models achieved high accuracy (84.33% for PGSI 5+, 82.52% for PGSI 8+).
  • Key predictors included betting frequency, variability, and repeat engagement.
  • Model performance was evaluated using receiver operating characteristic curves.

Conclusions:

  • Machine learning effectively classifies at-risk online gamblers using platform data.
  • Personalized harm prevention initiatives are feasible.
  • Model performance involves trade-offs between sensitivity and precision.