Benchmarking Machine Learning Architectures forMenstrual Recovery Prediction Using PhysiologicallyInformed Synthetic Wearable Data

Benchmarking Machine Learning Architectures forMenstrual Recovery Prediction Using PhysiologicallyInformed Synthetic Wearable 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 Benchmarking Machine Learning Architectures forMenstrual Recovery Prediction Using PhysiologicallyInformed Synthetic Wearable Data Lillian Shen, Pouria Mortezaagha, Arya Rahgozar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9430731/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Secondary amenorrhea is a heterogeneous condition with implications for reproductive, cardiovascular, and bone health. Existing machine learning approaches in menstrual health focus on cycle prediction rather than recovery modeling in pathological conditions. We present a proof-of-concept framework to model menstrual recovery within three months from non-invasive wearable-derived physiological features and self-reported inputs, including heart rate variability, resting heart rate, sleep, physical activity, skin temperature, perceived stress, age, and duration of amenorrhea. Using a synthetically generated dataset of 5,000 individuals encoding physiologically informed feature-outcome relationships, twelve models were evaluated across baseline and longitudinal configurations. The best-performing model (XGBoost) achieved AUC of 0.914, with ablation analysis confirming baseline features capturing the majority of learnable signal (∆AUC = 0.020). Permutation-based null models confirmed non-trivial predictive structure (AUC = 0.503), and XGBoost outperformed rule-based baselines (∆AUC = 0.044). SHapley Additive exPlanations (SHAP) analysis identified perceived stress and heart rate variability as dominant predictors, consistent with the data-generating structure. As the wearable-derived features are routinely captured by consumer devices and the self-reported inputs require brief periodic assessment, this framework establishes a foundation for wearable-based modeling of menstrual recovery, with future work required for real-world clinical validation and integration into wearable-based health monitoring systems. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Health care Health sciences/Medical research Biological sciences/Physiology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 21 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 15 Apr, 2026 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-9430731","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":628575684,"identity":"3fe73c4b-a684-4ac7-844e-6e2a11d499fc","order_by":0,"name":"Lillian Shen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACPmbmxgMJQIYBAwPjgw8VQBYzcwNeLWzMjA0wLcyGM86AtDAS0MIA1MIA0cImzdsGYhLSwg7U8qDmjr05e+9hw5nzaqP524FaflRsI+CwY88Sd/acS3zwcdvx3BmHGRsYe87cJqCF7XCCwY0cY8OZ247lNgC1MDO2EdLy77C9wf03ZtK8c47lzidKS2LbYcYNN3iAWhpqcjcQp6XvcOKGM0CHzTh2IHcjUMtBfH7h5z988OGPb0CHHT9j+OBDTV3uvPOHDz74UYFbCzo4DCYPEK0eCOpIUTwKRsEoGAUjBAAA6thjDlvIQR4AAAAASUVORK5CYII=","orcid":"","institution":"University of Ottawa","correspondingAuthor":true,"prefix":"","firstName":"Lillian","middleName":"","lastName":"Shen","suffix":""},{"id":628575685,"identity":"15e4beaa-44e8-4aef-b45c-706584b32c38","order_by":1,"name":"Pouria Mortezaagha","email":"","orcid":"","institution":"University of Ottawa","correspondingAuthor":false,"prefix":"","firstName":"Pouria","middleName":"","lastName":"Mortezaagha","suffix":""},{"id":628575686,"identity":"f8702bcc-ddb9-4362-b774-8840d2ad48c7","order_by":2,"name":"Arya Rahgozar","email":"","orcid":"","institution":"University of Ottawa","correspondingAuthor":false,"prefix":"","firstName":"Arya","middleName":"","lastName":"Rahgozar","suffix":""}],"badges":[],"createdAt":"2026-04-15 19:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9430731/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9430731/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870505,"identity":"9a0fe5c4-e4f9-4de8-89fd-f72e2a1f6e2e","added_by":"auto","created_at":"2026-04-27 07:39:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5168927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptsecondaryamenorrhea.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9430731/v1_covered_14e22c91-9c1c-47eb-994d-4939df236184.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Benchmarking Machine Learning Architectures forMenstrual Recovery Prediction Using PhysiologicallyInformed Synthetic Wearable Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"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":"","lastPublishedDoi":"10.21203/rs.3.rs-9430731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9430731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Secondary amenorrhea is a heterogeneous condition with implications for reproductive, cardiovascular, and bone health. Existing machine learning approaches in menstrual health focus on cycle prediction rather than recovery modeling in pathological conditions. We present a proof-of-concept framework to model menstrual recovery within three months from non-invasive wearable-derived physiological features and self-reported inputs, including heart rate variability, resting heart rate, sleep, physical activity, skin temperature, perceived stress, age, and duration of amenorrhea. Using a synthetically generated dataset of 5,000 individuals encoding physiologically informed feature-outcome relationships, twelve models were evaluated across baseline and longitudinal configurations. The best-performing model (XGBoost) achieved AUC of 0.914, with ablation analysis confirming baseline features capturing the majority of learnable signal (∆AUC = 0.020). Permutation-based null models confirmed non-trivial predictive structure (AUC = 0.503), and XGBoost outperformed rule-based baselines (∆AUC = 0.044). SHapley Additive exPlanations (SHAP) analysis identified perceived stress and heart rate variability as dominant predictors, consistent with the data-generating structure. 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