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Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
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Related Experiment Video

Updated: May 26, 2025

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Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm

Soeun Kim1,2, Hyejun Kim1,3, Seokjun Kim1,4

  • 1Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Seoul, Republic of Korea.

Journal of Medical Internet Research
|February 24, 2025
PubMed
Summary

Machine learning accurately predicts adolescent substance use across South Korea, the US, and Norway. Smoking status, BMI, and mental health factors significantly influence substance use risk.

Keywords:
MLNorwaySouth KoreaUnited StatesXGBoostadolescentadolescentsalcoholinterventioninterventionsmachine learningpredictionrandom forestrisk behaviorsmokingsubstancesubstance usesurveyweb-based survey

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Area of Science:

  • Adolescent Health
  • Machine Learning Applications
  • Cross-cultural Research

Background:

  • Gaps in understanding cultural and social variations in adolescent substance use.
  • Need for generalizable predictive models for adolescent substance use.

Purpose of the Study:

  • Develop a generalizable machine learning (ML) model to predict adolescent substance use.
  • Utilize multinational datasets to address cultural and social variations.

Main Methods:

  • Trained ML models using the Korea Youth Risk Behavior Web-Based Survey (KYRBS) (n=1,098,641).
  • Validated models externally using the US Youth Risk Behavior Survey (YRBS) (n=2,511,916) and Norwegian Ungdata surveys (n=700,660).
  • Assessed model performance using AUROC, precision, and other metrics; analyzed feature importance with SHapley Additive exPlanation (SHAP) values.

Main Results:

  • The XGBoost model demonstrated strong predictive performance across datasets.
  • Achieved an AUROC of 80.61% on KYRBS, 79.30% on YRBS, and 76.39% on Ungdata.
  • Identified smoking status, BMI, suicidal ideation, alcohol consumption, and negative emotions as key predictors, with smoking being most influential.

Conclusions:

  • Machine learning, specifically the XGBoost model, shows significant potential for predicting adolescent substance use.
  • Findings from multinational data provide a foundation for targeted interventions and further research into influencing factors.