[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117801-en":3,"doc-seo-117801-105":29,"detail-sidebar-cat-0-en-105":90},{"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117801,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",7,"Healthcare","Predicting total lung capacity from spirometry - a machine learning approach","Spirometry patterns can indicate restrictive ventilatory impairment, yet confirming diagnosis requires total lung capacity (TLC) measurements. This study trained supervised tree-based machine learning models using 51,761 spirometry data points with corresponding TLC values, then evaluated performance on an independent test set of 1,402 patients. The best model, CatBoost, predicted TLC with mean squared error (MSE) of 560.1 mL, and identified restrictive impairment with 83% sensitivity, 92% specificity, and 75% F1-score. The approach supports smarter decision-making and home-based monitoring for restrictive lung disease.","TYPE Original Research PUBLISHED 19 May 2023  \nDOI 10.3389/fmed.2023.1174631  \nOPEN ACCESS  \nEDITED BY  \nMd. Mohaimenul Islam,  \nThe Ohio State University, United States  \nREVIEWED BY  \nChandra Segar T,  \nVellore Institute of Technology (VIT), India Diana Calaras,  \nNicolae Testemiţanu State University of Medicine and Pharmacy, Moldova Christophe Delclaux,  \nHôpital Robert Debré, France  \n*CORRESPONDENCE  \nMaarten De Vos  \n [maarten.devos@kuleuven.be](maarten.devos@kuleuven.be)  \nRECEIVED 26 February 2023  \nACCEPTED 13 April 2023  \nPUBLISHED 19 May 2023  \nCITATION  \nBeverin L, Topalovic M, Halilovic A, Desbordes P, Janssens W and De Vos M (2023) Predicting total lung capacity from spirometry: a machine learning approach.  \nFront. Med. 10:1174631 .  \ndoi: 10.3389/fmed.2023.1174631  \nCOPYRIGHT  \n© 2023 Beverin, Topalovic, Halilovic, Desbordes, Janssens and De Vos. 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.  \nPredicting total lung capacity from spirometry: a machine learning approach  \nLuka Beverin 1, Marko Topalovic 2, Armin Halilovic 2,  \nPaul Desbordes 2, Wim Janssens3 and Maarten De Vos4, 5*  \n1Statistics Research Centre, KU Leuven, Leuven, Belgium, 2ArtiQ NV, Leuven, Belgium, 3 Laboratory of Respiratory Diseases and Thoracic Surgery, Department of Chronic Diseases Metabolism and Ageing, Ku Leuven, Leuven, Belgium, 4Stadius, Department of Electrical Engineering, KU Leuven, Leuven, Belgium, 5 Department of Development and Regeneration, KU Leuven, Leuven, Belgium  \nBackground and objective: Spirometry patterns can suggest that a patient has a restrictive ventilatory impairment; however, lung volume measurements such as total lung capacity (TLC) are required to confirm the diagnosis. The aim of the study was to train a supervised machine learning model that can accurately estimate TLC values from spirometry and subsequently identify which patients would most benefit from undergoing a complete pulmonary function test.  \nMethods: We trained three tree-based machine learning models on 51,761 spirometry data points with corresponding TLC measurements. We then compared model performance using an independent test set consisting of 1,402 patients. The best-performing model was used to retrospectively identify restrictive ventilatory impairment in the same test set. The algorithm was compared against different spirometry patterns commonly used to predict restriction.  \nResults: The prevalence of restrictive ventilatory impairment in the test set is 16.7%(234/1402) . CatBoost was the best-performing machine learning model. It predicted TLC with a mean squared error (MSE) of 560.1 mL. The sensitivity, specificity, and F1-score of the optimal algorithm for predicting restrictive ventilatory impairment was 83, 92, and 75%, respectively.  \nConclusion: A machine learning model trained on spirometry data can estimate TLC to a high degree of accuracy. This approach could be used to develop future smart home-based spirometry solutions, which could aid decision making and self-monitoring in patients with restrictive lung diseases.  \nKEYWORDS  \nrestriction, spirometry, machine learning, interstitial lung disease, total lung capacity  \n1. Introduction  \nRestrictive lung disorders are a group of conditions that affect the ability of the lungs to expand fully, resulting in reduced lung capacity and difficulty breathing. These conditions are typically caused by either intrinsic or extrinsic factors, such as interstitial lung diseases or chestwall problems ( 1). Patients with restrictive lung disorders often experience a decreased quality of life and in","cbCaimn3QBK0fdcz","https://ap.wps.com/l/cbCaimn3QBK0fdcz","pdf",1177064,1,"English","en",105,"# Introduction\n## Restrictive lung disorders and diagnostic role of TLC\n## Limits of standard TLC measurement in primary care\n## Home-based spirometry and machine learning opportunities\n# Methods\n## Training data and models\n## Performance comparison on an independent test set\n## Identification of restrictive ventilatory impairment\n# Results\n## Prevalence in the test set\n## TLC prediction accuracy\n## Classification performance for restriction\n# Conclusion","[{\"question\":\"What is the primary goal of the study?\",\"answer\":\"To train a supervised machine learning model that estimates total lung capacity (TLC) from spirometry, and then identifies which patients most benefit from complete pulmonary function testing.\"},{\"question\":\"How were the machine learning models developed and evaluated?\",\"answer\":\"Three tree-based models were trained on 51,761 spirometry records with TLC measurements and compared on an independent test set of 1,402 patients.\"},{\"question\":\"Which model performed best, and how accurate was it?\",\"answer\":\"CatBoost performed best, predicting TLC with a mean squared error of 560.1 mL and achieving 83% sensitivity, 92% specificity, and 75% F1-score for restrictive impairment detection.\"}]","Predicting total lung capacity from spirometry - a machine learning approach | PDF",1785679642,18,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"predicting-total-lung-capacity-from-spirometry-a-machine-learning-approach","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/predicting-total-lung-capacity-from-spirometry-a-machine-learning-approach/117801/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the primary goal of the study?","Question",{"text":74,"@type":75},"To train a supervised machine learning model that estimates total lung capacity (TLC) from spirometry, and then identifies which patients most benefit from complete pulmonary function testing.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How were the machine learning models developed and evaluated?",{"text":79,"@type":75},"Three tree-based models were trained on 51,761 spirometry records with TLC measurements and compared on an independent test set of 1,402 patients.",{"name":81,"@type":72,"acceptedAnswer":82},"Which model performed best, and how accurate was it?",{"text":83,"@type":75},"CatBoost performed best, predicting TLC with a mean squared error of 560.1 mL and achieving 83% sensitivity, 92% specificity, and 75% F1-score for restrictive impairment detection.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":45,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]