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...
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
August 2025
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| In: |
Addiction biology
Year: 2025, Volume: 30, Issue: 8, Pages: 1-11 |
| ISSN: | 1369-1600 |
| DOI: | 10.1111/adb.70056 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1111/adb.70056 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/adb.70056 
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| Author Notes: | Pablo Reinhardt, Norman Zacharias, Marinus Fislage, Justin Böhmer, Barbara Hollunder, Zala Reppmann, Anton Wiehe, Rebecca Rajwich, Nanne Dominick, Kerstin Ritter, Malek Bajbouj, Thomas Wienker, Jürgen Gallinat, Norbert Thürauf, Johannes Kornhuber, Falk Kiefer, Michael Wagner, Oliver Tüscher, Henrik Walter, Georg Winterer |
| Summary: | 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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| Item Description: | Erstmals veröffentlicht: 6.August 2025 Gesehen am 04.11.2025 |
| Physical Description: | Online Resource |
| ISSN: | 1369-1600 |
| DOI: | 10.1111/adb.70056 |