Using Classifiers to Identify Binge Drinkers Based on Drinking Motives

R. Crutzen, P. Giabbanelli

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)

Abstract

A representative sample of 2,844 Dutch adult drinkers completed a questionnaire on drinking motives and drinking behavior in January 2011. Results were classified using regressions, decision trees, and support vector machines (SVMs). Using SVMs, the mean absolute error was minimal, whereas performance on identifying binge drinkers was high. Moreover, when comparing the structure of classifiers, there were differences in which drinking motives contribute to the performance of classifiers. Thus, classifiers are worthwhile to be used in research regarding (addictive) behaviors, because they contribute to explaining behavior and they can give different insights from more traditional data analytical approaches.

Original languageEnglish
Pages (from-to)110-115
Number of pages6
JournalSubstance Use & Misuse
Volume49
Issue number1-2
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • classifiers
  • nonlinearity
  • identifying binge drinkers
  • drinking motives
  • ALCOHOL-USE
  • SUBSTANCE-ABUSE
  • ADOLESCENTS
  • CONSUMPTION
  • VALIDATION
  • ADULTS
  • MODEL
  • RISK

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