Predicting Edema in Liver Cirrhosis Patients: Development and Validation of Multivariable Machine Learning Models

Predicting Edema in Liver Cirrhosis Patients: Development and Validation of Multivariable 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 Research Article Predicting Edema in Liver Cirrhosis Patients: Development and Validation of Multivariable Machine Learning Models Helbi Mathew This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5739268/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: Edema is a medical condition characterized by the abnormal accumulation of fluid within body tissues, commonly affecting the extremities, such as legs and arms. It is a common complication of liver cirrhosis, a disease characterized by fibrosis and impaired liver function. Although edema may not pose an immediate threat to life, managing it is essential for reducing patient discomfort, keeping track of cirrhosis progression, and avoiding further complications. This study aims to construct machine learning models to predict the likelihood of edema in liver cirrhosis patients. Methods: This research uses data from a Mayo Clinic trial on primary biliary cirrhosis (1974-1984) with 424 patients. Eight classification algorithms—logistic regression, decision trees, support vector machine, random forests, neural networks, XGBoost, AdaBoost, and k-nearest neighbors—were evaluated for predicting the probability of edema. Correlations between the decisive attributes were also analyzed. Risk stratification was performed based on predicted probabilities from the top performing model. Results: Random Forest with 10-fold cross validation, exhibited better performance(accuracy > 90%) in identifying potential edema (precision > 80%), whereas neural network and logistic regression showcased better sensitivity (> 81%). Conclusion: Random forest model is the most suitable model to identify potential edema in cirrhosis patients. Such a system can help physicians to mitigate the risk of fluid imbalance early on. Edema liver cirrhosis machine learning predictive model Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5739268","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397686356,"identity":"1bc1da40-ab91-448f-ae89-5bbcdb13c5e7","order_by":0,"name":"Helbi Mathew","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACAwbmNoYEEEsCiD/AxdnwaWGEaOEBamGcAeQTp4UBqoWZhxgt5uyNbQ8eMGyTt5duPiZt8+cPA79E+jUJhjIbnFosew62GyQw3DbskTmWJp3bZsAgOSOnTILhXBpuh91IbJMAamHskcgxu53bYMBgcOZMmgRj22HcWu4/BGux75HI/3bb4g9cy388tjCCtSQCbWG7zcAG1HK8/RhQywHcWs4kAv1icDu550aa+c/eNmMeyfYeZouEc8m4tRw/fOzhj4rbtu0zkh8b/PgjJ8fPzP7wxocyO5xaoBoRTB4gMoCkBxIA+wMSNYyCUTAKRsEwBwAurlMphbARjQAAAABJRU5ErkJggg==","orcid":"","institution":"Otto-von-Guericke University Magdeburg","correspondingAuthor":true,"prefix":"","firstName":"Helbi","middleName":"","lastName":"Mathew","suffix":""}],"badges":[],"createdAt":"2024-12-31 05:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5739268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5739268/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73139078,"identity":"20abdb70-a662-4328-b493-26bf6ed97b6b","added_by":"auto","created_at":"2025-01-07 06:37:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":850216,"visible":true,"origin":"","legend":"","description":"","filename":"EdemaPredictionMLModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5739268/v1_covered_63b34be7-e49f-489d-81b6-23bea9189ca2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Edema in Liver Cirrhosis Patients: Development and Validation of Multivariable Machine Learning Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Edema, liver cirrhosis, machine learning, predictive model","lastPublishedDoi":"10.21203/rs.3.rs-5739268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5739268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Edema is a medical condition characterized by the abnormal accumulation of fluid within body tissues, commonly affecting the extremities, such as legs and arms. It is a common complication of liver cirrhosis, a disease characterized by fibrosis and impaired liver function. Although edema may not pose an immediate threat to life, managing it is essential for reducing patient discomfort, keeping track of cirrhosis progression, and avoiding further complications. This study aims to construct machine learning models to predict the likelihood of edema in liver cirrhosis patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This research uses data from a Mayo Clinic trial on primary biliary cirrhosis (1974-1984) with 424 patients. Eight classification algorithms—logistic regression, decision trees, support vector machine, random forests, neural networks, XGBoost, AdaBoost, and k-nearest neighbors—were evaluated for predicting the probability of edema. Correlations between the decisive attributes were also analyzed. 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