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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\u2010based cohort to identify multimodal markers that distinguish smokers from never smokers at baseline and predict long\u2010term cessation success at a 10\u2010year follow\u2010up. We employed\u00a010 times repeated nested cross\u2010validation (10 outer folds, 5 inner folds) to analyse baseline data (T1) from 707 smokers\u2014including 222 heavy smokers (FTND \u2265 4)\u2014and 864 never smokers for smoking status classification. At the 10\u2010year follow\u2010up (T2), we further classified 60 successful quitters (\u2265\u20091 year abstinent) versus 81 non\u2010quitters. 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 \u00b1 SD): smokers versus never smokers, 0.85\u2009\u00b1\u20090.03; heavy smokers versus never smokers, 0.92\u2009\u00b1\u20090.03; and quitters versus non\u2010quitters, 0.68\u2009\u00b1\u20090.13. 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.<\/jats:p>","DOI":"10.1111\/adb.70056","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T04:29:02Z","timestamp":1754540942000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Classification of Smoking Behaviours\u2014From Social Environment to the Prefrontal Cortex"],"prefix":"10.1111","volume":"30","author":[{"given":"Pablo","family":"Reinhardt","sequence":"first","affiliation":[{"name":"Department of Psychiatry and Psychotherapy Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin  Berlin Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5794-5716","authenticated-orcid":false,"given":"Norman","family":"Zacharias","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin  Berlin Germany"},{"name":"Department of Anesthesiology and Intraoperative Intensive Care Medicine Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin  Berlin Germany"},{"name":"Pharmaimage Biomarker Solutions Inc.  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