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Using Classifiers to Identify Binge Drinkers Based on Drinking Motives.

Rik Crutzen¹, Philippe Giabbanelli²

¹a 1Maastricht University/CAPHRI , Maastricht, Netherlands.

Substance Use & Misuse

|August 23, 2013

Summary

This summary is machine-generated.

Classifiers like support vector machines (SVMs) effectively identified binge drinkers in a Dutch study. These machine learning models offer valuable insights into addictive behaviors beyond traditional methods.

Keywords:

classifiers drinking motives identifying binge drinkers nonlinearity

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Area of Science:

Behavioral Science
Data Science
Addiction Research

Background:

Understanding the complex relationship between drinking motives and alcohol consumption patterns is crucial.
Traditional data analysis methods may not fully capture the nuances of addictive behaviors.

Purpose of the Study:

To evaluate the effectiveness of machine learning classifiers, including support vector machines (SVMs), in analyzing drinking motives and behavior.
To compare the performance of different classification methods in identifying binge drinkers.

Main Methods:

A representative sample of 2,844 Dutch adult drinkers completed a questionnaire on drinking motives and behavior in January 2011.
Data were analyzed using regressions, decision trees, and support vector machines (SVMs).

Main Results:

Support vector machines (SVMs) demonstrated minimal mean absolute error and high performance in identifying binge drinkers.
Analysis revealed differences in how specific drinking motives contributed to the performance of various classifiers.

Conclusions:

Machine learning classifiers are valuable tools for addiction research, offering unique insights into behavior.
These methods enhance the explanation of (addictive) behaviors compared to traditional data analytical approaches.