A Machine Learning Approach to Identify Predictors of Frequent Vaping and Vulnerable Californian Youth Subgroups.

ABSTRACT:

Introduction

Machine learning presents a unique opportunity to improve electronic cigarette (vaping) monitoring in youth. Here we built a random forest model to predict frequent vaping status among Californian youth and to identify contributing factors and vulnerable populations.

Methods

In this prospective cohort study, 1281 ever-vaping twelfth-grade students from metropolitan Los Angeles were surveyed in Fall and in 6-month in Spring. Frequent vaping was measured at the 6-month follow-up as nicotine-containing vaping on 20 or more days in past 30 days. Predictors (n = 131) encompassed sociodemographic characteristics, substance use and perceptions, health status, and characteristics of the household, school, and neighborhood. A random forest was developed to identify the t

SUBMITTER: Fu R 

PROVIDER: S-EPMC9199938 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature