25: Machine Learning of Smoking Relapse

Music City Center 

Machine learning model can help identify multifaceted factors influencing tobacco transitions. A random forest model is developed to predict smoking relapse, focusing on racial disparities and vaping characteristics. Data are drawn from the Population Assessment of Tobacco and Health (PATH) Study adult interview files. Former combustible cigarette smokers at baseline (Wave 5) were followed up one year later (Wave 6). Predictors (n=100) include a wide range of social demographics, psychosocial factors, health status, tobacco and substance use behaviors, and vaping characteristics. The findings reveal notable racial disparities in smoking relapse predictors, along with distinct roles of vaping characteristics across racial groups. Unique social, behavioral, and health factors are crucial for improving smoking cessation outcomes.

e-cigarettes

random forest

PATH study 

Section on Statistics in Epidemiology