Development and validation of a machine learning model based on multiple kernel for predicting the recurrence  risk of Budd-Chiari syndrome 

Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome | 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 Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome Weirong Xue, Yingliang Jin, Shengli Li, Bing Xu, Hui Wang, Xiaoxiao Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4673014/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 Background Budd-Chiari syndrome (BCS) is a rare condition worldwide with a high recurrence rate. The existing prognostic scoring models have shown limited predictive efficacy for recurrence of BCS patients.The study aim to establish a more effective machine learning model based on multiple kernel learning for predicting the recurrence of Budd-Chiari syndrome patients within three years. Methods The dataset was obtained from patients diagnosed with BCS admitted to the Affiliated Hospital of Xuzhou Medical University between January 2015 and July 2022.The data were divided into training, validation, and test sets in a 6:2:2 ratio. We established respective model based on traversal of all combinations of four kernel functions in training set, and selected best hyperparameters for each model by particle swarm optimization (PSO) algorithm in validation set. Test set was conducted for comparasion of kernel function combinations, with AUC (area under the curve), sensitivity, specificity, and accuracy used as evaluation indexs. The optimal model, utilizing the best-selected kernel combination, was then compared with three other machine learning models to further assess its performance. Result A kernel combination incorporating all four basic kernels achieved the highest average AUC, specificity, and accuracy, as well as a slightly lower mean but more stable sensitivity across all combinations. In comparison with other classical machine learning models, our model also achieved significant advantages in performance. Furthermore, it outperformed previous studies with similar objectives. Conclusion We have explored risk factors influencing relapse of BCS patients and demonstrated our proposed MKSVRB model is superior to previous prediction methods and other machine learning models, showcasing its significant potential in early detection, determination, and prevention of relapse in patients with Budd-Chiari syndrome. Health sciences/Health care Health sciences/Risk factors Health sciences/Medical research/Epidemiology Health sciences/Medical research/Experimental models of disease Health sciences/Medical research/Outcomes research Budd–Chiari syndrome Recurrence Machine Learning Multiple Kernel Learning Predict 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4673014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":330367562,"identity":"fd9c9d3f-c4f5-4d4a-8128-e4eeadff75c3","order_by":0,"name":"Weirong Xue","email":"","orcid":"","institution":"Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Weirong","middleName":"","lastName":"Xue","suffix":""},{"id":330367563,"identity":"0e124a3a-4ca5-43f2-87b3-df7937cfdfa0","order_by":1,"name":"Yingliang Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJACxoYKGx5+9sbGBx+I13ImTU6y53Cz4QyitTS2HTY2uJHeJs1BjHL52c3PPs5sS0vccPNhgzQDg52cbgMBLQZ3jhnP3HDOJnHm7cQG4wKGZGOzA4S0SCQYMz4oS0vsA2pJnsFwIHEbIS3yM9I/Mz5gO5zYcPNgw2EeYrQw3MgxZtwA9L7ADcbGZqK0GNw5U8w4AxzIic2MMwyI8Iv87PbNjD3gqDz+/MeHCjs5gloYJFAtJaQcU8soGAWjYBSMAiwAAD6ES1hgdnnJAAAAAElFTkSuQmCC","orcid":"","institution":"Xuzhou Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yingliang","middleName":"","lastName":"Jin","suffix":""},{"id":330367564,"identity":"46fea7a0-8262-4317-bbc8-4d07428d0394","order_by":2,"name":"Shengli Li","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Shengli","middleName":"","lastName":"Li","suffix":""},{"id":330367565,"identity":"d5d77044-5190-4872-bc18-41b3d35492ea","order_by":3,"name":"Bing Xu","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Xu","suffix":""},{"id":330367566,"identity":"01d80ab4-1455-4cf8-9c55-9f40869a5a14","order_by":4,"name":"Hui Wang","email":"","orcid":"","institution":"Affiliated Hospital of Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":330367567,"identity":"5c439ea0-f9bc-4c4c-8784-426869255446","order_by":5,"name":"Xiaoxiao Zhu","email":"","orcid":"","institution":"Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxiao","middleName":"","lastName":"Zhu","suffix":""},{"id":330367568,"identity":"32a96923-92e9-4368-b6d0-5adf0b280c2e","order_by":6,"name":"Jiajia Qin","email":"","orcid":"","institution":"Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jiajia","middleName":"","lastName":"Qin","suffix":""},{"id":330367569,"identity":"da93bae7-ad3f-46af-b61c-5ea326b547b8","order_by":7,"name":"Peilin Yu","email":"","orcid":"","institution":"Xuzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Peilin","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-07-02 09:17:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4673014/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4673014/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74425417,"identity":"acbce1ce-0aa3-42e0-bb46-6c219e167674","added_by":"auto","created_at":"2025-01-22 07:54:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1292226,"visible":true,"origin":"","legend":"","description":"","filename":"Developmentandvalidationofamachinelearningmodel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4673014/v1_covered_69866ad8-fc40-4fbf-865a-12acd41d71ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Budd–Chiari syndrome, Recurrence, Machine Learning, Multiple Kernel Learning, Predict","lastPublishedDoi":"10.21203/rs.3.rs-4673014/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4673014/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBudd-Chiari syndrome (BCS) is a rare condition worldwide with a high recurrence rate. The existing prognostic scoring models have shown limited predictive efficacy for recurrence of BCS patients.The study aim to establish a more effective machine learning model based on multiple kernel learning for predicting the recurrence of Budd-Chiari syndrome patients within three years.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe dataset was obtained from patients diagnosed with BCS admitted to the Affiliated Hospital of Xuzhou Medical University between January 2015 and July 2022.The data were divided into training, validation, and test sets in a 6:2:2 ratio. We established respective model based on traversal of all combinations of four kernel functions in training set, and selected best hyperparameters for each model by particle swarm optimization (PSO) algorithm in validation set. Test set was conducted for comparasion of kernel function combinations, with AUC (area under the curve), sensitivity, specificity, and accuracy used as evaluation indexs. The optimal model, utilizing the best-selected kernel combination, was then compared with three other machine learning models to further assess its performance.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eA kernel combination incorporating all four basic kernels achieved the highest average AUC, specificity, and accuracy, as well as a slightly lower mean but more stable sensitivity across all combinations. In comparison with other classical machine learning models, our model also achieved significant advantages in performance. Furthermore, it outperformed previous studies with similar objectives.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe have explored risk factors influencing relapse of BCS patients and demonstrated our proposed MKSVRB model is superior to previous prediction methods and other machine learning models, showcasing its significant potential in early detection, determination, and prevention of relapse in patients with Budd-Chiari syndrome.\u003c/p\u003e","manuscriptTitle":"Development and validation of a machine learning model based on multiple kernel for predicting the recurrence risk of Budd-Chiari syndrome ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 11:59:17","doi":"10.21203/rs.3.rs-4673014/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"a19bdbdd-c80a-4ebf-8c08-bfbc8b7b7293","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34972601,"name":"Health sciences/Health care"},{"id":34972602,"name":"Health sciences/Risk factors"},{"id":34972603,"name":"Health sciences/Medical research/Epidemiology"},{"id":34972604,"name":"Health sciences/Medical research/Experimental models of disease"},{"id":34972605,"name":"Health sciences/Medical research/Outcomes research"}],"tags":[],"updatedAt":"2025-01-22T07:54:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-29 11:59:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4673014","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4673014","identity":"rs-4673014","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}