Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study.

ABSTRACT:

Background

To address gaps in global understanding of cultural and social variations, this study used a high-performance machine learning (ML) model to predict adolescent substance use across three national datasets.

Objective

This study aims to develop a generalizable predictive model for adolescent substance use using multinational datasets and ML.

Methods

The study used the Korea Youth Risk Behavior Web-Based Survey (KYRBS) from South Korea (n=1,098,641) to train ML models. For external validation, we used the Youth Risk Behavior Survey (YRBS) from the United States (n=2,511,916) and Norwegian nationwide Ungdata surveys (Ungdata) from Norway (n=700,660). After developing various ML models, we evaluated the final model's performance using multiple metrics. We also a

SUBMITTER: Kim S 

PROVIDER: S-EPMC11894353 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature