Machine Learning Classification of Smoking Behaviours-From Social Environment to the Prefrontal Cortex.

ABSTRACT: The pronounced heterogeneity in smoking trajectories-ranging from occasional or heavy use to successful quitting -highlights substantial interindividual variation within the smoking population. Machine learning is particularly well suited to capture these complex patterns that may be challenging for traditional inferential statistics to uncover. In this study, we applied machine learning to data from a population-based cohort to identify multimodal markers that distinguish smokers from never smokers at baseline and predict long-term cessation success at a 10-year follow-up. We employed 10 times repeated nested cross-validation (10 outer folds, 5 inner folds) to analyse baseline data (T1) from 707 smokers-including 222 heavy smokers (FTND ≥ 4)-and 864 never smokers for smoking status classi

SUBMITTER: Reinhardt P 

PROVIDER: S-EPMC12328245 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature