Prediction of Chronic Obstructive Pulmonary Disease Using Machine Learning Models

Prediction of Chronic Obstructive Pulmonary Disease Using Machine Learning Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prediction of Chronic Obstructive Pulmonary Disease Using Machine Learning Models Sher Ali, Omair Faqah, Elise Neubarth, Mohammad Shehroz Ashraf, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8246175/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Chronic Obstructive Pulmonary Disease (COPD) is a progressive lung condition that causes restricted airflow and breathing problems in patients. The disease has remained a leading cause of mortality worldwide, yet early prediction of at-risk individuals remains a challenge. Traditional diagnostic approaches rely on symptomatic assessment or expensive, inaccessible clinical tests rather than predictive modeling, which delays disease intervention. This study explores the potential of machine learning in predicting COPD risk by utilizing the extensive All of Us database, which provides diverse health data. Using a cohort of 42,941 individuals, we extracted demographic, lifestyle, and clinical features that are relevant to COPD susceptibility in the literature. Extensive data processing techniques were utilized that involved handling missing values, feature selection, and normalization. Feature importance analysis highlighted smoking history, environmental exposures, and comorbidities as key contributors to COPD risk. Various machine learning algorithms, including random forest, multi-layer perceptron, and support vector machine, were trained and validated to assess the predictive performance of our framework. Performance evaluation based on accuracy and area under the receiver operating characteristic curve (AUC-ROC) metrics indicates that the random forest model outperformed the conventional statistical methods with an accuracy of 83% and an AUC-ROC of 0.89. While some prior studies report higher AUC-ROC, those often rely on specialized data (e.g., imaging, genetic, or questionnaire-based inputs) and small or imbalanced datasets. In contrast, our model achieves competitive performance using a reduced, accessible clinical feature set across a large, diverse cohort. Our findings suggest that machine learning-based predictive models can greatly enhance the early identification of at-risk individuals to allow targeted interventions if needed. By integrating such predictive analytics into healthcare systems, we hope to shift focus to more proactive risk mitigation in COPD care. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Health sciences/Risk factors Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 Feb, 2026 Reviews received at journal 07 Jan, 2026 Reviews received at journal 19 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviews received at journal 13 Dec, 2025 Reviewers agreed at journal 13 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Editor invited by journal 10 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 09 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8246175","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":559684871,"identity":"d6006375-88cb-426e-9f5a-df649f1ddf78","order_by":0,"name":"Sher Ali","email":"","orcid":"","institution":"Florida Atlantic University","correspondingAuthor":false,"prefix":"","firstName":"Sher","middleName":"","lastName":"Ali","suffix":""},{"id":559684872,"identity":"d64b5124-5f89-47ee-9a4f-dabdda3de088","order_by":1,"name":"Omair Faqah","email":"","orcid":"","institution":"Florida Atlantic 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The disease has remained a leading cause of mortality worldwide, yet early prediction of at-risk individuals remains a challenge. Traditional diagnostic approaches rely on symptomatic assessment or expensive, inaccessible clinical tests rather than predictive modeling, which delays disease intervention. This study explores the potential of machine learning in predicting COPD risk by utilizing the extensive \u003cem\u003eAll of Us\u003c/em\u003e database, which provides diverse health data. Using a cohort of 42,941 individuals, we extracted demographic, lifestyle, and clinical features that are relevant to COPD susceptibility in the literature. Extensive data processing techniques were utilized that involved handling missing values, feature selection, and normalization. Feature importance analysis highlighted smoking history, environmental exposures, and comorbidities as key contributors to COPD risk. 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