[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125383-en":3,"doc-seo-125383-105":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},125383,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine learning classification models for predicting chronic pain - Research study","Chronic pain is a widespread condition that substantially impairs daily functioning through persistent sensory and emotional discomfort. This study predicts chronic pain using post-traumatic stress disorder (PTSD), alexithymia, anxiety, pain catastrophizing, stress, depression, and demographic variables. Data from 234 males and 307 females in Tehran (2022–2023) were analyzed to classify pain severity into three levels (low, medium, high). PTSD and alexithymia were the strongest predictors, while the SGB model achieved the best performance, supported by SHAP findings emphasizing psychological factors.","Machine learning classification models for predicting chronic pain  \nNataša Kovač1 · Kruna Ratković1 · Peter Watson2 · Hojjatollah Farahani3 · Farzin Bagheri Sheykhangafshe3  \nAccepted: 22 July 2025 / Published online: 6 August 2025 © The Author(s) 2025  \nAbstract  \nChronic pain is a widespread condition that profoundly affects the daily functioning of many people worldwide, characterized by persistent sensory and emotional discomfort associated with real or perceived tissue injury. This study aims to predict chronic pain based on post-traumatic stress disorder (PTSD), alexithymia, anxiety, pain catastrophizing, stress, depression, and demographic variables as correlates. The analysis included data from 234 males and 307 females experiencing chronic pain in Tehran province between 2022 and 2023. The classification results suggested that PTSD and alexithymia were the most significant predictors, followed by anxiety, depression, and pain catastrophizing. Six different machine learning (ML) classification techniques: Naive Bayes, Decision Tree, Gradient Boosting, Light Gradient Boosting Machine, Support Vector Machine, and Stochastic Gradient Boosting (SGB) were applied to examine a dataset detailing various dimensions associated with chronic pain. The findings in the study classified pain severity into three levels, low, medium, and high, based on quantiles and used six ML models to predict these classes. The SGB model outperformed the others, showing higher accuracy and F1 scores, particularly in predicting the medium pain class. SHAP analysis revealed that psychological factors such as alexithymia, anxiety, PTSD, depression, and stress were significant predictors of pain severity, while age and gender had less impact.  \nKeywords Chronic pain · Classification · Machine learning · Health psychology · Medical psychology  \nNataša Kovač, Kruna Ratković, Peter Watson and Hojjatollah Farahani contributed equally to this work.  \n􀀍 Peter Watson [peter.watson@mrc-cbu.cam.ac.uk](peter.watson@mrc-cbu.cam.ac.uk)  \nNataša Kovač  \n[natasa.kovac@udg.edu.me](natasa.kovac@udg.edu.me)  \nKruna Ratković  \n[kruna.ratkovic@udg.edu.me](kruna.ratkovic@udg.edu.me)  \nHojjatollah Farahani  \n[h.farahani@modares.ac.ir](h.farahani@modares.ac.ir)  \nFarzin Bagheri Sheykhangafshe  \n[farzinbagheri@modares.ac.ir](farzinbagheri@modares.ac.ir)  \n1 Department of Mathematics, Faculty of Applied Sciences, University of Donja Gorica, Oktoih 1, Podgorica 81000, Montenegro  \n2 MRC Cognition and Brain Sciences Unit, Cambridge University, 15 Chaucer Road, Cambridge  \nCB2 7EF, United Kingdom  \n3 Department of Psychology, Tarbiat Modares University, Jalal AleAhmed, Tehran 14115-111, Nasr, Iran  \nIntroduction  \nChronic pain is a widespread and often incapacitating condition that substantially diminishes the quality of life for countless individuals across the globe, impacting not only physical health but also emotional, social, and functional aspects of daily living (Miaskowski et al., 2020) . The origins of chronic pain can vary widely, from injuries and illnesses to more obscure causes, and its effects extend beyond the individual to families, workplaces, and healthcare systems (Raffaeli et al., 2021) . Chronic pain’s far-reaching impact makes it a significant public health issue requiring comprehensive management strategies.  \nResearch on chronic pain’s prevalence across different populations emphasizes its global burden. For instance, Ghafouri et al. (2022) conducted a large-scale study in Iran, which revealed a 25.2% prevalence of chronic pain among participants, particularly affecting women, older adults, smokers, and individuals with low physical activity or inadequate sleep. Similarly, Todd et al. (2019) found high prevalence rates across Europe, with back and neck pain being most common, impacting about 40%of the population,  \nfollowed by hand/arm and foot/leg pain at 22% and 21%, respectively. These studies highlight chronic pain as a global health concern that aff","cbCail6egzL6O8Ip","https://ap.wps.com/l/cbCail6egzL6O8Ip","pdf",1471502,1,14,"English","en",105,"# Introduction\n## Global burden and prevalence of chronic pain\n## Multifactorial nature of chronic pain\n## Role of machine learning in chronic pain prediction\n## Key assessment instruments (GCPS, DASS-21, PAQ)","[{\"question\":\"What variables are used to predict chronic pain severity?\",\"answer\":\"The study uses PTSD, alexithymia, anxiety, pain catastrophizing, stress, depression, and demographic variables as correlates to predict chronic pain severity.\"},{\"question\":\"How is chronic pain severity categorized in the classification task?\",\"answer\":\"Chronic pain severity is classified into three levels—low, medium, and high—based on quantiles.\"},{\"question\":\"Which machine learning model performed best and what did SHAP indicate?\",\"answer\":\"Stochastic Gradient Boosting (SGB) outperformed the other models with higher accuracy and F1 scores, especially for the medium pain class. SHAP showed psychological factors such as alexithymia, anxiety, PTSD, depression, and stress as significant predictors.\"}]","Machine learning classification models for predicting chronic pain - Research study | PDF",1785898590,35,{"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-models-for-predicting-chronic-pain-research-study","",{"@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-models-for-predicting-chronic-pain-research-study/125383/",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","2026-08-05",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 variables are used to predict chronic pain severity?","Question",{"text":75,"@type":76},"The study uses PTSD, alexithymia, anxiety, pain catastrophizing, stress, depression, and demographic variables as correlates to predict chronic pain severity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is chronic pain severity categorized in the classification task?",{"text":80,"@type":76},"Chronic pain severity is classified into three levels—low, medium, and high—based on quantiles.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and what did SHAP indicate?",{"text":84,"@type":76},"Stochastic Gradient Boosting (SGB) outperformed the other models with higher accuracy and F1 scores, especially for the medium pain class. 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