Machine Learning Workflow for Correlating Anxiety and Stress: A SHAP-Based Multimodal Analysis

Machine Learning Workflow for Correlating Anxiety and Stress: A SHAP-Based Multimodal Analysis | 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 Machine Learning Workflow for Correlating Anxiety and Stress: A SHAP-Based Multimodal Analysis Anirudh Sowrirajan, Pranav Srinivasan, Sundari Avanthikaa Srinivasan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6254760/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 Many studies employ black-box Machine Learning (ML) models to classify stress and anxiety without examining the underlying biological and phys iological relevance. In this study, we developed an ML workflow based on Shap ley values (SHAP) to interpret black-box models. This approach enables model agnostic visualization of complex relationships between features and predictions while facilitating the explanation of individual predictions, which is essential in clinical practice. To demonstrate the workflow, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Decision Tree (CART), and Random izedSearchCV-optimized models were trained on the Multilevel Monitoring of Activity and Sleep in Healthy People (MMASH) dataset. Participants were sub grouped into high and low state anxiety groups, where heart rate was predicted within each subgroup. SHAP analysis identified activity type as the most influ ential feature distinguishing anxiety states. Additionally, stress-inducing versus rest activity classification was performed using features including RMSSD, heart rate, and sleep-related measures, with heart rate emerging as the most significant attribute. The RF and XGBoost classifiers achieved an area under the ROC curve (AUC-ROC) exceeding 0.998 for stress-versus-rest classification. Key features influencing anxiety and stress classifications included heart rate, sleep efficiency, melatonin levels before sleep, and cortisol levels after sleep. The results highlight a direct correlation between stress and anxiety, emphasizing the potential of mul timodal data integration for clinical assessments and personalized interventions. Interpretable Machine learning Stress Anxiety Heart rate Shapley values Multilevel monitoring of activity and sleep in healthy people Full Text Additional Declarations The authors declare no competing interests. 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-6254760","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430597853,"identity":"d471ec9c-401e-4cab-a8e7-2926e753e9e5","order_by":0,"name":"Anirudh Sowrirajan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYFACxgYGBgO2+v3HgTSDgQXRWvgYG84cAGmRINoqOcaGGwkgBhFa+Gc3N3+uKDBjZpz5/OqGHwUSDPzt3Ql4tUjcOdgmecYgjY1ZOqfsZg/QYRJnzm7Ab82NxDbGBoNjPGzSOWk3eIBaDCRy8WuRv5HY/LHB4L8Ej+SZtJt/iNFicCOxQbLBgM1AQoL92G2ibDEEOgykJcGAJ4fttoyBBA9Bv8jdSH/8seEPUAv78Wc33/yxkeNv7yXgfQTgMQCTxCoHAfYHpKgeBaNgFIyCEQQAD/pGdaZF+zMAAAAASUVORK5CYII=","orcid":"","institution":"SRMIST","correspondingAuthor":true,"prefix":"","firstName":"Anirudh","middleName":"","lastName":"Sowrirajan","suffix":""},{"id":430597854,"identity":"a7cf49aa-36ce-451c-81d8-570da8a9374f","order_by":1,"name":"Pranav Srinivasan","email":"","orcid":"","institution":"SRMIST","correspondingAuthor":false,"prefix":"","firstName":"Pranav","middleName":"","lastName":"Srinivasan","suffix":""},{"id":430597855,"identity":"7ac00f01-db73-4048-8e2a-1ee0252435dd","order_by":2,"name":"Sundari Avanthikaa Srinivasan","email":"","orcid":"","institution":"SRMIST","correspondingAuthor":false,"prefix":"","firstName":"Sundari","middleName":"Avanthikaa","lastName":"Srinivasan","suffix":""},{"id":430597856,"identity":"e06301ee-a8fb-4068-9c15-0ba6de2b2e64","order_by":3,"name":"S Sridhar","email":"","orcid":"","institution":"SRMIST","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Sridhar","suffix":""}],"badges":[],"createdAt":"2025-03-18 15:47:16","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6254760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6254760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78894346,"identity":"1b059cd6-46cd-4ef3-98e7-61e380c9f2b1","added_by":"auto","created_at":"2025-03-20 12:00:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":688803,"visible":true,"origin":"","legend":"","description":"","filename":"Machinelearningworkflow.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6254760/v1_covered_792bd82c-9f5b-4cc0-a53f-eb5d32e8eb43.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMachine Learning Workflow for Correlating Anxiety and Stress: A SHAP-Based Multimodal Analysis\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SRM Institute of Science and Technology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":" Interpretable Machine learning, Stress, Anxiety, Heart rate, Shapley values, Multilevel monitoring of activity and sleep in healthy people ","lastPublishedDoi":"10.21203/rs.3.rs-6254760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6254760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMany studies employ black-box Machine Learning (ML) models to \u0026nbsp;classify stress and anxiety without examining the underlying biological and phys iological relevance. In this study, we developed an ML workflow based on Shap ley values (SHAP) to interpret black-box models. This approach enables model agnostic visualization of complex relationships between features and predictions \u0026nbsp;while facilitating the explanation of individual predictions, which is essential in \u0026nbsp;clinical practice. To demonstrate the workflow, Random Forest (RF), eXtreme \u0026nbsp;Gradient Boosting (XGBoost), Decision Tree (CART), and Random izedSearchCV-optimized models were trained on the Multilevel Monitoring of \u0026nbsp;Activity and Sleep in Healthy People (MMASH) dataset. Participants were sub grouped into high and low state anxiety groups, where heart rate was predicted \u0026nbsp;within each subgroup. SHAP analysis identified activity type as the most influ ential feature distinguishing anxiety states. Additionally, stress-inducing versus \u0026nbsp;rest activity classification was performed using features including RMSSD, heart \u0026nbsp;rate, and sleep-related measures, with heart rate emerging as the most significant \u0026nbsp;attribute. The RF and XGBoost classifiers achieved an area under the ROC curve \u0026nbsp;(AUC-ROC) exceeding 0.998 for stress-versus-rest classification. Key features \u0026nbsp;influencing anxiety and stress classifications included heart rate, sleep efficiency, \u0026nbsp;melatonin levels before sleep, and cortisol levels after sleep. The results highlight \u0026nbsp;a direct correlation between stress and anxiety, emphasizing the potential of mul timodal data integration for clinical assessments and personalized interventions.\u003c/p\u003e","manuscriptTitle":"Machine Learning Workflow for Correlating Anxiety and Stress: A SHAP-Based Multimodal Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-20 11:52:00","doi":"10.21203/rs.3.rs-6254760/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":"f1f7adb7-a8f2-4c86-a376-3cdf7218412a","owner":[],"postedDate":"March 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-20T11:52:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-20 11:52:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6254760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6254760","identity":"rs-6254760","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}