An Explainable Ensemble Learning -Based Auxiliary Diagnosis System for Cerebral Small Vessel Disease

An Explainable Ensemble Learning -Based Auxiliary Diagnosis System for Cerebral Small Vessel Disease | 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 An Explainable Ensemble Learning -Based Auxiliary Diagnosis System for Cerebral Small Vessel Disease Benben Wang, Yuefei Yan Yan, Baoqing Han, Huizhen Lu, Hao Wang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7717465/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Cerebral small vessel disease (CSVD) poses major public health challenges, yet current MRI based diagnosis detects only established damage, and existing auxiliary methods, mostly based on conventional statistics, lack sufficient feature extraction capability and generalizability, thereby limiting early warning and precision management. Accordingly, we developed an intelligent auxiliary diagnostic system grounded in an interpretable ensemble learning framework, aiming to enable early detection and warning of CSVD. To support this development, a total of 597 sets of electronic medical record data from Quzhou Affiliated Hospital of Wenzhou Medical University were used as the study cohort. Firstly, a multidimensional feature evaluation and selection method was proposed, identifying 12 key predictive factors out of 23 relevant variables. Subsequently, the optimal algorithm was selected from Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting Machine, XGBoost, and Multilayer Perceptron Classifier, based on Area Under the Curve (AUC) and Accuracy metrics, and a stacking ensemble learning strategy was then employed for model construction. The developed model demonstrated excellent discriminative performance, achieving an AUC of 0.924 while maintaining a low Brier score of 0.1065. By integrating the SHAP interpretability algorithm, the model provided intuitive visualizations of feature importance, thereby enhancing transparency and facilitating clinical adoption. Ultimately, this study achieved effective integration of early warning and auxiliary diagnostic functions for CSVD. These results indicate that the proposed system possesses high accuracy, interpretability, and deployability, underscoring its broad potential for early warning and personalized management of CSVD. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Cerebral small vessel disease Auxiliary diagnosis system Early warning Interpretable ensemble learning Key factor Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 25 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 29 Sep, 2025 Submission checks completed at journal 26 Sep, 2025 First submitted to journal 26 Sep, 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. We do this by developing innovative software and high quality services for the global research community. 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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-7717465","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534788275,"identity":"43f8b95e-2bd5-4973-925f-8ab531943a29","order_by":0,"name":"Benben Wang","email":"","orcid":"","institution":"Xidian University","correspondingAuthor":false,"prefix":"","firstName":"Benben","middleName":"","lastName":"Wang","suffix":""},{"id":534788276,"identity":"bfd0ec35-6881-4706-91c2-dfb20c1c7f13","order_by":1,"name":"Yuefei Yan 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Auxiliary diagnosis system, Early warning, Interpretable ensemble learning, Key factor","lastPublishedDoi":"10.21203/rs.3.rs-7717465/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7717465/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCerebral small vessel disease (CSVD) poses major public health challenges, yet current MRI based diagnosis detects only established damage, and existing auxiliary methods, mostly based on conventional statistics, lack sufficient feature extraction capability and generalizability, thereby limiting early warning and precision management. Accordingly, we developed an intelligent auxiliary diagnostic system grounded in an interpretable ensemble learning framework, aiming to enable early detection and warning of CSVD. To support this development, a total of 597 sets of electronic medical record data from Quzhou Affiliated Hospital of Wenzhou Medical University were used as the study cohort. Firstly, a multidimensional feature evaluation and selection method was proposed, identifying 12 key predictive factors out of 23 relevant variables. Subsequently, the optimal algorithm was selected from Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting Machine, XGBoost, and Multilayer Perceptron Classifier, based on Area Under the Curve (AUC) and Accuracy metrics, and a stacking ensemble learning strategy was then employed for model construction. The developed model demonstrated excellent discriminative performance, achieving an AUC of 0.924 while maintaining a low Brier score of 0.1065. By integrating the SHAP interpretability algorithm, the model provided intuitive visualizations of feature importance, thereby enhancing transparency and facilitating clinical adoption. Ultimately, this study achieved effective integration of early warning and auxiliary diagnostic functions for CSVD. These results indicate that the proposed system possesses high accuracy, interpretability, and deployability, underscoring its broad potential for early warning and personalized management of CSVD.\u003c/p\u003e","manuscriptTitle":"An Explainable Ensemble Learning -Based Auxiliary Diagnosis System for Cerebral Small Vessel Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 12:18:21","doi":"10.21203/rs.3.rs-7717465/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T18:13:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-25T04:58:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281554857274357545398582523962653119857","date":"2025-10-24T17:14:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T20:52:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229561494210705637986450143365776362837","date":"2025-10-14T13:57:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-14T05:15:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T07:15:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-26T13:37:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-26T04:00:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9439a761-21e1-4b0f-bee6-4e78caddebcf","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":56858964,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56858965,"name":"Health sciences/Diseases"},{"id":56858966,"name":"Physical sciences/Engineering"},{"id":56858967,"name":"Health sciences/Health care"},{"id":56858968,"name":"Physical sciences/Mathematics and computing"},{"id":56858969,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-11T07:58:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 12:18:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7717465","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7717465","identity":"rs-7717465","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}