Application of Machine Learning in Early Screening of Yu Disease: Model Construction and Analysis Based on Routine Laboratory Data

Application of Machine Learning in Early Screening of Yu Disease: Model Construction and Analysis Based on Routine Laboratory Data | 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 Application of Machine Learning in Early Screening of Yu Disease: Model Construction and Analysis Based on Routine Laboratory Data Jie Su, Ruihuan Zhang, Hongliang Sun, Wenzhong Li, Chao Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8153499/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Background Yu disease, a representative "Shenzhi Disease" in Traditional Chinese Medicine (TCM), is characterized by liver qi stagnation, emotional depression, and chest/hypochondriac distension, but lacks objective early screening tools. This study pioneers an interpretable machine learning (ML) model that converts "qi-blood disharmony"(a TCM concept) into quantifiable biomarkers, using only routine blood biochemical indicators to enable low-cost early warning. Methods Clinical data of 3,347 patients (including those with Yu disease and non-Yu disease controls) were collected from Chifeng Mental Health Prevention and Treatment Hospital, covering the period from March 2013 to September 2019. The dataset included demographic information and routine laboratory test results. After data cleaning, 54 features were retained for baseline analysis, and 16 optimal features were selected using the backward elimination method. Four ML algorithms-Deep Neural Network (DNN), Extreme gradient boosting (XGBoost), Logistic Regression (LR), and Support Vector Machine (SVM)- were employed to build Yu disease prediction models. Model performance was evaluated via cross-validation. To intuitively interpret the XGBoost model results, the Shapley Additive exPlanations (SHAP) method was used. Results The XGBoost model outperformed the other models, achieving an accuracy of 0.904, sensitivity of 0.886, specificity of 0.915, and a Receiver Operating Characteristic curve area (ROC-AUC) of 0.964. SHAP analysis identified the key features: albumin, basophil percentage, platelet distribution width, age, globulin, indirect bilirubin that influenced the predictions. Conclusion This study successfully developed a high-accuracy early screening model for Yu disease using routine laboratory data, providing a new tool for clinical practice. Future studies should focus on multicenter validation and the incorporation of additional biomarkers to enhance model generalizability. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Medical research Yu disease Machine Learning Blood Routine Prediction model SHAP analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Dec, 2025 Reviews received at journal 29 Dec, 2025 Reviews received at journal 25 Dec, 2025 Reviewers agreed at journal 25 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviewers agreed at journal 22 Dec, 2025 Reviews received at journal 19 Dec, 2025 Reviews received at journal 18 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviews received at journal 10 Dec, 2025 Reviewers agreed at journal 04 Dec, 2025 Reviewers invited by journal 03 Dec, 2025 Editor invited by journal 28 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 19 Nov, 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. 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-8153499","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555439287,"identity":"506d396f-90a7-4b96-8727-cbd5c30f9fba","order_by":0,"name":"Jie Su","email":"","orcid":"","institution":"Inner Mongolia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Su","suffix":""},{"id":555439288,"identity":"3473e4f4-3948-444e-a686-6be188016896","order_by":1,"name":"Ruihuan Zhang","email":"","orcid":"","institution":"Medical Intelligent Diagnostics Big Data Research 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Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Yu disease, Machine Learning, Blood Routine, Prediction model, SHAP analysis","lastPublishedDoi":"10.21203/rs.3.rs-8153499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8153499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eYu disease, a representative \"Shenzhi Disease\" in Traditional Chinese Medicine (TCM), is characterized by liver qi stagnation, emotional depression, and chest/hypochondriac distension, but lacks objective early screening tools. This study pioneers an interpretable machine learning (ML) model that converts \"qi-blood disharmony\"(a TCM concept) into quantifiable biomarkers, using only routine blood biochemical indicators to enable low-cost early warning.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eClinical data of 3,347 patients (including those with Yu disease and non-Yu disease controls) were collected from Chifeng Mental Health Prevention and Treatment Hospital, covering the period from March 2013 to September 2019. The dataset included demographic information and routine laboratory test results. After data cleaning, 54 features were retained for baseline analysis, and 16 optimal features were selected using the backward elimination method. Four ML algorithms-Deep Neural Network (DNN), Extreme gradient boosting (XGBoost), Logistic Regression (LR), and Support Vector Machine (SVM)- were employed to build Yu disease prediction models. Model performance was evaluated via cross-validation. To intuitively interpret the XGBoost model results, the Shapley Additive exPlanations (SHAP) method was used.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe XGBoost model outperformed the other models, achieving an accuracy of 0.904, sensitivity of 0.886, specificity of 0.915, and a Receiver Operating Characteristic curve area (ROC-AUC) of 0.964. SHAP analysis identified the key features: albumin, basophil percentage, platelet distribution width, age, globulin, indirect bilirubin that influenced the predictions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study successfully developed a high-accuracy early screening model for Yu disease using routine laboratory data, providing a new tool for clinical practice. Future studies should focus on multicenter validation and the incorporation of additional biomarkers to enhance model generalizability.\u003c/p\u003e","manuscriptTitle":"Application of Machine Learning in Early Screening of Yu Disease: Model Construction and Analysis Based on Routine Laboratory Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 16:53:52","doi":"10.21203/rs.3.rs-8153499/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T07:39:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T08:14:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-25T10:10:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280666096529627292557918258339645820342","date":"2025-12-25T10:01:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61842547999823834340793372859184796340","date":"2025-12-22T15:12:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211220514074225386357368261903880385143","date":"2025-12-22T13:59:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-19T12:47:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T08:14:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"340188918200967734416323802914164202629","date":"2025-12-17T11:17:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94470508802768407432870584663362534261","date":"2025-12-17T03:02:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35133600193101865579512031113901520256","date":"2025-12-17T02:50:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153523495298339621801757442726678351558","date":"2025-12-12T13:15:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-10T08:27:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110542564198260120533805243279438495119","date":"2025-12-04T18:54:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-04T02:23:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-28T11:53:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T11:51:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T11:50:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-19T09:22:44+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":"ddb0f56f-dba0-4fb5-899c-c99e5b9aac51","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59118074,"name":"Health sciences/Biomarkers"},{"id":59118075,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59118076,"name":"Health sciences/Diseases"},{"id":59118077,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-03-26T06:40:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-05 16:53:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8153499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8153499","identity":"rs-8153499","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}