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

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 classification. At the 10-year follow-up (T2), we further classified 60 successful quitters (≥ 1 year abstinent) versus 81 non-quitters. Feature importance was assessed using averaged SHAP values derived from test set predictions. Classification models achieved the following performance, expressed by the area under the receiver operating characteristic curve (AUROC; mean ± SD): smokers versus never smokers, 0.85 ± 0.03; heavy smokers versus never smokers, 0.92 ± 0.03; and quitters versus non-quitters, 0.68 ± 0.13. SHAP analysis identified markers of frontal functioning, cognitive control and smoking behaviour within the social environment among the most influential predictors of both smoking status and cessation success. In conclusion, our machine learning analyses support a multifactorial model of smoking behaviour and cessation success, which may inform nuanced risk stratification to advance the development of personalized cessation strategies.

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Publication Details

Journal
Addiction Biology
Published
2025-08-01
DOI
https://doi.org/10.1111/adb.70056
Citations
1
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
2.12

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article

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

Justin Böhmer, Nanne Dominick, Norbert Thürauf, Zala Reppmann et al.
1 citations
Addiction Biology
Mental Health Research Topics
2.12
article

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

Justin Böhmer, Nanne Dominick, Norbert Thürauf, Zala Reppmann, Jürgen Gallinat, Norman Zacharias, Michael Wagner, Malek Bajbouj, Barbara Hollunder, Marinus Fislage, Falk Kiefer, Pablo Reinhardt, Kerstin Ritter, Johannes Kornhuber, Oliver Tuescher, Georg Winterer, Henrik Walter, Thomas F. Wienker, Anton Wiehe, Rebecca Rajwich
article en
1 citations

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 classification. At the 10-year follow-up (T2), we further classified 60 successful quitters (≥ 1 year abstinent) versus 81 non-quitters. Feature importance was assessed using averaged SHAP values derived from test set predictions. Classification models achieved the following performance, expressed by the area under the receiver operating characteristic curve (AUROC; mean ± SD): smokers versus never smokers, 0.85 ± 0.03; heavy smokers versus never smokers, 0.92 ± 0.03; and quitters versus non-quitters, 0.68 ± 0.13. SHAP analysis identified markers of frontal functioning, cognitive control and smoking behaviour within the social environment among the most influential predictors of both smoking status and cessation success. In conclusion, our machine learning analyses support a multifactorial model of smoking behaviour and cessation success, which may inform nuanced risk stratification to advance the development of personalized cessation strategies.

Addiction BiologyVol. 30(8)
Universität Hamburg (DE), University of Applied Sciences Mainz (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Johannes Gutenberg University Mainz (DE), Heidelberg University (DE), University Hospital Bonn (DE), University Hospital Heidelberg (DE), Humboldt-Universität zu Berlin (DE), Biomark (United States) (US), Central Institute of Mental Health (DE), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), Hertie Institute for Clinical Brain Research (DE), Max Planck Institute for Molecular Genetics (DE), Einstein Center for Neurosciences Berlin (DE), HAW Hamburg (DE), Charité - Universitätsmedizin Berlin (DE)
Deutsche Forschungsgemeinschaft, Berlin Institute of Health
Good health and well-being
Openalex Percentile: Top 12%
Mental Health Research Topics
2.12
DOIOpen Access PDFOpenAlex
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