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Related Concept Videos

Binge Eating Disorders01:23

Binge Eating Disorders

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Binge eating disorder is a significant mental health condition characterized by recurrent episodes of excessive food consumption within a short period, accompanied by a perceived loss of control over eating behavior. Unlike occasional overeating, binge eating disorder is marked by distressing emotions such as guilt, shame, and anxiety following binge episodes. The disorder affects individuals across different ages and backgrounds, with profound implications for physical and psychological...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Related Experiment Video

Updated: Oct 18, 2025

Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder
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Binge drinking in early adulthood: A machine learning approach.

Nathaniel A Dell1, Sweta Prasad Srivastava2, Michael G Vaughn1

  • 1School of Social Work, Saint Louis University, Saint Louis, MO, United States.

Addictive Behaviors
|October 1, 2021
PubMed
Summary
This summary is machine-generated.

Young adults exhibit varied binge drinking behaviors. Random forest analysis effectively identified correlates like risky behaviors and substance dependence, outperforming other methods in classification accuracy.

Keywords:
Binge drinkingMachine learningRandom forestYoung adults

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Area of Science:

  • Public Health
  • Psychology
  • Data Science

Background:

  • Binge drinking is a significant public health issue among young adults (18-25).
  • Diverse drinking patterns exist within this demographic.
  • Identifying accurate correlates of binge drinking is crucial for intervention.

Purpose of the Study:

  • To compare the efficacy of logistic regression, classification trees, and random forests in identifying binge drinking correlates.
  • To determine the most accurate machine learning method for classifying binge drinking behavior.

Main Methods:

  • Evaluated three statistical methods: logistic regression, classification trees, and random forests.
  • Analyzed correlates including propensity for risky behaviors, marijuana and cocaine dependence, ethnicity, and education level.
  • Utilized Area Under the Curve (AUC) analysis to compare model performance.

Main Results:

  • All models identified similar correlates: risky behavior propensity, marijuana/cocaine dependence, non-Hispanic white ethnicity, and higher education.
  • Random forests demonstrated superior accuracy in classifying positive binge drinking cases compared to logistic regression and classification trees.
  • Machine learning, specifically random forests, is a viable approach for analyzing binge drinking correlates.

Conclusions:

  • Random forests modeling of psychosocial data effectively identifies binge drinking correlates in young adults.
  • The findings support the use of advanced machine learning techniques in public health research.
  • Clinical implications include enhanced screening protocols for binge drinking in behavioral health settings.