Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation.

Journal: Addiction (Abingdon, England)
PMID: 30461117

Abstract

BACKGROUND AND AIMS: The experience of alcohol use among adolescents is complex, with international differences in age of purchase and individual differences in consumption and consequences. This latter underlines the importance of prediction modeling of adolescent alcohol use. The current study (a) compared the performance of seven machine-learning algorithms to predict different levels of alcohol use in mid-adolescence and (b) used a cross-cultural cross-study scheme in the training-validation-test process to display the predictive power of the best performing machine-learning algorithm.

Authors

  • Mohammad H Afzali
    Department of Psychiatry, University of Montreal, Montréal, QC, Canada.
  • Matthew Sunderland
    National Drug and Alcohol Research Centre, University of New South Wales, Randwick, NSW, Australia.
  • Sherry Stewart
    Department of Psychiatry, Dalhousie University, Life Sciences Centre-Psychology, Halifax, NS, Canada.
  • Benoit Masse
  • Jean Seguin
    Department of Psychiatry, University of Montreal, Montréal, QC, Canada.
  • Nicola Newton
    National Drug and Alcohol Research Centre, University of New South Wales, Randwick, NSW, Australia.
  • Maree Teesson
    National Drug and Alcohol Research Centre, University of New South Wales, Randwick, NSW, Australia.
  • Patricia Conrod
    Department of Psychiatry, University of Montreal, Montréal, QC, Canada.

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