[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125483-en":3,"doc-seo-125483-105":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},125483,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Using machine learning to develop Iraqi-specific spirometric reference equations - Research highlights","spirometry is essential for diagnosing and managing respiratory diseases, and its accuracy depends on reference equations that match the studied population’s lung function. This study develops sex-specific spirometric reference equations for healthy Iraqi adults using machine learning and evaluates performance against the Global Lung Function (GLI) 2012 and 2022 equations. The approach trains multiple models on age and height, selects the best performer, and reports improved accuracy and calibration for FeV1 and FVC.","Future Science OA  \nISSN: 2056-5623 (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/ifso20)[www.tandfonline.com/journals/ifso20](homepage: www.tandfonline.com/journals/ifso20)  \nUsing machine learning to develop Iraqi-specificspirometric reference equations  \nWalid Al-Qerem, Alaa Alsaj ri, AnanJarab & Judith Eberhardt  \nTo cite this article: Walid Al-Qerem, Alaa Alsaj ri, AnanJarab & Judith Eberhardt (2025) Using machine learning to develop Iraqi-specific spirometric reference equations, Future Science OA, 11:1, 2582429, DOI: 10. 1080/20565623 .2025.2582429  \nTo link to this article: [https://doi.org/10.1080/20565623.2025.2582429](https://doi.org/10.1080/20565623.2025.2582429)  \n© 2025 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group  \n\n|  View supplementary material  |  |\n| --- | --- |\n|  | Published online: 05 Nov 2025. |\n|  | Submit your article to this journal  |\n|  | View related articles  |\n|  View Crossmark data |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=ifso20](https://www.tandfonline.com/action/journalInformation?journalCode=ifso20)  \nFuture Science OA  \n2025, VOL. 11, nO. 1, 2582429  \n[https://doi.org/10.1080/20565623.2025.2582429](https://doi.org/10.1080/20565623.2025.2582429)  \nReseaRch aRticle  \nUsing machine learning to develop Iraqi-specific spirometric reference equations  \nWalid al-Qerema, alaa alsajrib, anan Jarabc and Judith eberhardtd   \naDepartment of Pharmacy, Faculty of Pharmacy, Al-Zaytoonah university of Jordan, Amman, Jordan; bSchool of Pharmaceutical Sciences, university Sains Malaysia, Penang, Malaysia; cDepartment of clinical Pharmacy, Faculty of Pharmacy, Jordan university of Science and technology, irbid, Jordan; dDepartment of Psychology, School of Social Sciences, Humanities and Law, teesside university, Middlesbrough, united Kingdom  \nABSTRACT  \nBackground: spirometry is essential for diagnosing and managing respiratory diseases. accurate interpretation relies on reference equations that reflect population-specific lung function. Global equations, such as those from the Global lung Function initiative (Gli), may not suit all populations, including iraqis.  \nObjective: to develop sex-specific spirometric reference equations for healthy iraqi adults using machine learning (Ml) and compare their performance with the Gli 2012 and 2022 equations.  \nMethods: this cross-sectional study included 3,959 healthy, nonsmoking iraqi adults aged ≥18 years. spirometry was performed per ats/eRs guidelines. Five Ml models (linear regression, random forest, support vector machine, gradient boosting machine (GBM), and k-nearest neighbors) were trained using age and height. Data were split into training (70%) and validation (30%) sets. Performance was assessed using RMse, R2, and z-score calibration. GBMwas selected as the best model.  \nResults: GBM outperformed all other models and Gli [equations. in](equations. in) females, R2 was 0.4473 for FeV1 and 0.4519 for FVc; in males, 0.3509 and 0.3674, respectively. Gli equations underestimated lung volumes, while GBM predictions were well calibrated with mean z-scores near zero.  \nConclusion: GBM-derived equations show improved accuracy and calibration over Gli standards for iraqi adults, offering a more suitable tool for spirometry interpretation.  \nPLAIN LANGUAGE SUMMARY  \nthis study used advanced computer methods called machine learning to create new equations that show what normal lung function looks like in healthy iraqi adults. these equations help doctors interpret breathing tests more accurately than the international standards currently used, which do not fit the iraqi population well. By training the models on data from healthy iraqi men and women, the study found that these local equations provide a better match and reduce the chance of misdiagnosis. this approach could also be used in other countries to make breathing ","cbCaiehd1gR5j2hD","https://ap.wps.com/l/cbCaiehd1gR5j2hD","pdf",2058409,1,13,"English","en",105,"# Abstract\n## Background and objective\n## Methods\n## Results and conclusion","[{\"question\":\"为什么需要为伊拉克人建立特异性的肺活量计参考方程？\",\"answer\":\"因为肺功能解释依赖反映人群差异的参考方程，而通用的GLI方程可能不适合伊拉克人群。\"},{\"question\":\"研究如何建立新的参考方程？\",\"answer\":\"使用多种机器学习模型（如线性回归、随机森林、支持向量机、梯度提升机和k近邻），并用年龄与身高训练。\"},{\"question\":\"结果表明哪种模型表现最好？\",\"answer\":\"梯度提升机（GBM）在各模型中表现最佳，且相较GLI标准具有更好的预测准确性与校准效果。\"}]","Using machine learning to develop Iraqi-specific spirometric reference equations - Research highlights | PDF",1785899255,33,{"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},"using-machine-learning-to-develop-iraqi-specific-spirometric-reference-equations-research-highlights","",{"@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/using-machine-learning-to-develop-iraqi-specific-spirometric-reference-equations-research-highlights/125483/",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},"为什么需要为伊拉克人建立特异性的肺活量计参考方程？","Question",{"text":75,"@type":76},"因为肺功能解释依赖反映人群差异的参考方程，而通用的GLI方程可能不适合伊拉克人群。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"研究如何建立新的参考方程？",{"text":80,"@type":76},"使用多种机器学习模型（如线性回归、随机森林、支持向量机、梯度提升机和k近邻），并用年龄与身高训练。",{"name":82,"@type":73,"acceptedAnswer":83},"结果表明哪种模型表现最好？",{"text":84,"@type":76},"梯度提升机（GBM）在各模型中表现最佳，且相较GLI标准具有更好的预测准确性与校准效果。","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"]