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A rapid, accessible diagnostic approach is needed for early detection and to limit ongoing TB transmission. Six supervised machine-learning models were developed from routine laboratory indicators using a discovery cohort of 3,829 individuals and externally validated on 405.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/validation-and-interpretation-of-machine-learning-models-for-rapid-identification-of-active-tuberculosis-infection-using-routine-laboratory-indicators/410839/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/validation-and-interpretation-of-machine-learning-models-for-rapid-identification-of-active-tuberculosis-infection-using-routine-laboratory-indicators/410839.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn Holloway","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-07","2026-09-28",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is there a need for rapid identification of active Mtb infection?","Question",{"text":113,"@type":114},"Active Mtb detection is essential for early treatment and for interrupting transmission chains, but conventional diagnostic approaches can be costly and slow.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were the machine-learning models validated in the study?",{"text":118,"@type":114},"Six supervised models were trained on routine laboratory data using a discovery cohort of 3,829 individuals and validated externally on 405 individuals.",{"name":120,"@type":111,"acceptedAnswer":121},"Which model performed best and what did SHAP show?",{"text":122,"@type":114},"XGBoost showed the best diagnostic performance in both internal and external cohorts, and SHAP indicated key predictors aligned with host-response patterns such as hypoalbuminemia, altered lipid metabolism, platelet activity changes, and lymphocyte reduction.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},410839,1791165273,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},2336474466712,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","TYPE Original Research PUBLISHED 18 December 2025 DOI 10.3389/fcimb.2025.1718614  \nOPEN ACCESS  \nEDITED BY  \nYing Luo,  \nUniversity of Texas Southwestern Medical Center, United States  \nREVIEWED BY  \nElena Niculet,  \nDunarea de Jos University, Romania Fu Gao,  \nYale University, United States Panpan Gu,  \nFudan University, China  \n*CORRESPONDENCE  \nLiang Wang  \n [healthscience@foxmail.com](healthscience@foxmail.com)[ ](healthscience@foxmail.com)Hui-Jin Chen  \n [13699994292@163.com](13699994292@163.com)[ ](13699994292@163.com)Kang-Peng Du  \n [kangpengdu@163.com](kangpengdu@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 04 October 2025  \nREVISED 27 November 2025  \nACCEPTED 30 November 2025  \nPUBLISHED 18 December 2025  \nCITATION  \nLiu Z-Z, Yuan Q, Zhang Y-D, Zhang X-D, Liu J, Yan J-W, Du K-P, Chen H-J and Wang L (2025) Validation and interpretation of machine-learning models for rapid identification of active tuberculosis infection using routine laboratory indicators.  \nFront. Cell. Infect. Microbiol. 15:1718614 .  \ndoi: 10.3389/fcimb.2025.1718614  \nCOPYRIGHT  \n© 2025 Liu, Yuan, Zhang, Zhang, Liu, Yan, Du, Chen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nValidation and interpretation of machine-learning models for rapid identification of active tuberculosis infection using routine laboratory indicators  \nZhan-Zhong Liu 1†, Quan Yuan 1,2,3†, Yu-Dong Zhang 4†, Xue-Di Zhang 1, Jian Liu 5, Jia-Wei Yan 1, Kang-Peng Du 5*, Hui-Jin Chen 2* and Liang Wang 3,6*  \n1thXuzhou Hospital, Beijing Ditan Hospital Affiliated to Capital Medical University, Xuzhou Infectious Diseases Hospital (The 7th People ’s Hospital of Xuzhou), Xuzhou, Jiangsu, China, 2 Department of Laboratory Medicine, Shengli Oilfield Central Hospital, Dongying, Shandong, China, 3School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou, Jiangsu, China, 4School of 1st Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China, 5 Department of Pharmacy, The 6th People’s Hospital of Xuzhou, Xuzhou, Jiangsu, China, 6 Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China  \nIntroduction: Diagnosis of active Mycobacterium tuberculosis (Mtb) infection relies on clinical symptoms, imaging, and molecular testing, but these methods are often costly and slow. Consequently, there is an urgent need for a rapid and accessible diagnostic approach that can support early detection and reduce ongoing tuberculosis transmission.  \nMethods: A discovery cohort of 3,829 individuals and an external validation cohort of 405 individuals were included. Six supervised machine learning models were trained using routine laboratory data, and model interpretability was assessed with SHapley Additive exPlanations (SHAP) .  \nResults: Among the six models, XGBoost demonstrated the best diagnostic performance in the internal cohort (accuracy 97.49%; sensitivity 97.56%; specificity 97.42%) and maintained strong performance in the external cohort (accuracy 93 . 67%; sensitivity 91 . 56%; specificity 91 . 13%) . SHAP analysis indicated that key predictors reflected characteristic host-response patterns, including inflammation-related hypoalbuminemia, lipid metabolism suppression (HDL-Cand LDL-C), altered platelet activity (MPV), and lymphocyte reduction (LYM) . Conclusion: The study presents a high-performing and interpretable machine learning model capable of accurately identifying active Mtb infection using routine blood tests. This low-cost and no","cbCailEG57aY9ICg","https://ap.wps.com/l/cbCailEG57aY9ICg","pdf",7462690,13,"English","# Introduction\n# Methods\n# Results\n# Conclusion\n# Graphical abstract\n## Background","[{\"question\":\"Why is there a need for rapid identification of active Mtb infection?\",\"answer\":\"Active Mtb detection is essential for early treatment and for interrupting transmission chains, but conventional diagnostic approaches can be costly and slow.\"},{\"question\":\"How were the machine-learning models validated in the study?\",\"answer\":\"Six supervised models were trained on routine laboratory data using a discovery cohort of 3,829 individuals and validated externally on 405 individuals.\"},{\"question\":\"Which model performed best and what did SHAP show?\",\"answer\":\"XGBoost showed the best diagnostic performance in both internal and external cohorts, and SHAP indicated key predictors aligned with host-response patterns such as hypoalbuminemia, altered lipid metabolism, platelet activity changes, and lymphocyte reduction.\"}]","Validation and interpretation of machine-learning models for rapid identification of active tuberculosis infection using routine laboratory indicators | PDF",1790586579,33]