[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121489-en":3,"doc-seo-121489-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121489,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Machine Learning Classification of Smoking Behaviours - From Social Environment to the Prefrontal Cortex","Smoking trajectories vary markedly, from occasional or heavy use to successful quitting, indicating substantial interindividual heterogeneity. Machine learning can capture complex patterns that traditional inferential statistics may struggle to resolve. Using a population-based cohort, this study applies machine learning to identify multimodal baseline markers distinguishing smokers from never smokers and predicting 10-year cessation success. Nested cross-validation evaluates classification performance and SHAP feature importance, highlighting frontal functioning, cognitive control, and social-environment smoking behavior as key predictors.","Addiction Biology  \nORIGINAL ARTICLE  OPEN ACCESS   \nMachine Learning Classification of Smoking Behaviours—From Social Environment to the  \nPrefrontal Cortex  \nPablo Reinhardt1 | Norman Zacharias2,3,4  | Marinus Fislage3  | Justin Böhmer1  | Barbara Hollunder5,6,7  | Zala Reppmann1 | Anton Wiehe4 | Rebecca Rajwich1  | Nanne Dominick3 | Kerstin Ritter1,5,8 | Malek Bajbouj1 | Thomas Wienker9 | Jürgen Gallinat10 | Norbert Thürauf11 | Johannes Kornhuber11 | Falk Kiefer12  | Michael Wagner13 | Oliver Tüscher14 | Henrik Walter1 | Georg Winterer3,4  \n1Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany | 2Department of Otolaryngology, Charité – Universitätsmedizin Berlin, Berlin, Germany | 3Department of Anesthesiology and Intraoperative Intensive Care Medicine, Charité – Universitätsmedizin Berlin, Berlin, Germany | 4Pharmaimage Biomarker Solutions Inc. , Cambridge, Massachusetts, USA | 5Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany | 6Einstein Center for Neurosciences Berlin, Charité – Universitätsmedizin Berlin, Berlin, Germany | 7Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany | 8Hertie Institute for AI in Brain Health, University ofTübingen,  \nGermany | 9Department of Molecular Human Genetics, Max Planck Institute for Molecular Genetics, Berlin, Germany | 10Department of Psychiatry, University Hospital Hamburg, Hamburg, Germany | 11Department of Psychiatry and Psychotherapy, University Clinic, Friedrich-Alexander-University of Erlangen-Nuremberg, Erlangen, Germany | 12Department of Addictive Behaviour and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany | 13Department of Psychiatry, University Hospital Bonn, Bonn, Germany | 14Department of Psychiatry, University Hospital Mainz, Mainz, Germany  \nCorrespondence: Georg Winterer ([georg.winterer@pi-pharmaimage.com](georg.winterer@pi-pharmaimage.com))  \nReceived: 9 January 2025 | Revised: 21 May 2025 | Accepted: 28 May 2025  \nFunding: This work received support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): National priority program SPP1226 ‘Nicotine: Physiological and Molecular Effect in the CNS’ (Wi1316/9-1) and Transregio TRR 265 (Project-ID 402170461) .  \nKeywords: classification | prefrontal function | tobacco use behaviour  \nABSTRACT  \nThe 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 anal","cbCaipWwwBl9oj2c","https://ap.wps.com/l/cbCaipWwwBl9oj2c","pdf",2485587,1,11,"English","en",105,"# Abstract\n## Background and rationale\n## Study design and data\n## Model evaluation and feature interpretation\n## Results and implications","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To use machine learning to classify smoking status and to predict long-term cessation success using multimodal baseline markers.\"},{\"question\":\"Which time points are used for prediction and outcome assessment?\",\"answer\":\"Baseline data are analyzed at T1, and cessation outcomes are assessed at the 10-year follow-up T2.\"},{\"question\":\"How are important predictors identified in the models?\",\"answer\":\"Feature importance is evaluated using averaged SHAP values derived from test-set predictions.\"}]","Machine Learning Classification of Smoking Behaviours - From Social Environment to the Prefrontal Cortex | PDF",1785735897,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-classification-of-smoking-behaviours-from-social-environment-to-the-prefrontal-cortex","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-classification-of-smoking-behaviours-from-social-environment-to-the-prefrontal-cortex/121489/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To use machine learning to classify smoking status and to predict long-term cessation success using multimodal baseline markers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which time points are used for prediction and outcome assessment?",{"text":80,"@type":76},"Baseline data are analyzed at T1, and cessation outcomes are assessed at the 10-year follow-up T2.",{"name":82,"@type":73,"acceptedAnswer":83},"How are important predictors identified in the models?",{"text":84,"@type":76},"Feature importance is evaluated using averaged SHAP values derived from test-set predictions.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]