Explainable AI for Population Mental Well-being Surveillance Using Community Health Survey Data

Explainable AI for Population Mental Well-being Surveillance Using Community Health Survey 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 Explainable AI for Population Mental Well-being Surveillance Using Community Health Survey Data Md Anisur Rahman, Pubudu Sanjeewani, Asanka Perera, Azadeh Alavi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8670939/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Population mental well-being surveillance increasingly relies on machine learning methods. However, most existing studies focus on predictive accuracy using black-box models, offering limited transparency and producing findings that are difficult to translate into public health action. This study proposes an explainable AI framework for identifying high-confidence, human-readable patterns of mental well-being outcomes using the Canadian Community Health Survey 2019-2020 data. We analysed 108252 anonymised survey records with approximately 50 attributes capturing demographics, chronic conditions, lifestyle behaviours, and psychosocial factors. After removing non-informative attributes and consolidating sparse response categories, we trained interpretable C4.5 decision tree models for four outcomes: pain status, stress level, work stress, and the Health Utility Index. The proposed approach achieved strong predictive performance across outcomes, with accuracies of 87.0% (pain), 82.1% (stress), 95.76% (work stress), and 82.3% (functional health). To enable actionable surveillance insights, all decision paths were automatically extracted as rules and annotated with support and confidence, revealing consistent co-occurrence patterns linking functional limitations and musculoskeletal conditions with pain, and highlighting associations between life satisfaction, sense of belonging, age strata, and elevated stress even among respondents reporting good mental health. Overall, the findings demonstrate that large-scale national health surveys can be effectively leveraged for explainable mental well-being surveillance, delivering interpretable evidence to support population risk stratification, early-warning monitoring, and policy-relevant public health interventions. Health sciences/Health care Biological sciences/Psychology Social science/Psychology Mental Health Data Mining Pattern Analysis Depression Pain Stress Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers agreed at journal 28 Feb, 2026 Reviewers invited by journal 05 Feb, 2026 Editor assigned by journal 05 Feb, 2026 Editor invited by journal 27 Jan, 2026 Submission checks completed at journal 26 Jan, 2026 First submitted to journal 26 Jan, 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-8670939","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":587441792,"identity":"e15e3084-7848-44ef-8abf-77d7e14a9400","order_by":0,"name":"Md Anisur Rahman","email":"data:image/png;base64,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","orcid":"","institution":"La Trobe University","correspondingAuthor":true,"prefix":"","firstName":"Md","middleName":"Anisur","lastName":"Rahman","suffix":""},{"id":587441793,"identity":"ad9f7c66-7a15-47d2-a6fa-834ffc3ff77c","order_by":1,"name":"Pubudu Sanjeewani","email":"","orcid":"","institution":"RMIT University","correspondingAuthor":false,"prefix":"","firstName":"Pubudu","middleName":"","lastName":"Sanjeewani","suffix":""},{"id":587441797,"identity":"6934c0d1-0777-40af-b045-a3645253f944","order_by":2,"name":"Asanka Perera","email":"","orcid":"","institution":"University of Southern Queensland","correspondingAuthor":false,"prefix":"","firstName":"Asanka","middleName":"","lastName":"Perera","suffix":""},{"id":587441800,"identity":"be261009-fb2e-48d4-bdcd-8f3ca59f878d","order_by":3,"name":"Azadeh Alavi","email":"","orcid":"","institution":"RMIT University","correspondingAuthor":false,"prefix":"","firstName":"Azadeh","middleName":"","lastName":"Alavi","suffix":""},{"id":587441801,"identity":"053e89e1-5c0c-4bf4-9472-03f15ecfc03a","order_by":4,"name":"Uffe Kock Wiil","email":"","orcid":"","institution":"University of Southern Denmark","correspondingAuthor":false,"prefix":"","firstName":"Uffe","middleName":"Kock","lastName":"Wiil","suffix":""}],"badges":[],"createdAt":"2026-01-22 14:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8670939/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8670939/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102297876,"identity":"ee9f872c-483e-42a3-b690-28c414c3abf7","added_by":"auto","created_at":"2026-02-10 10:29:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":504553,"visible":true,"origin":"","legend":"","description":"","filename":"MH.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8670939/v1_covered_1eb63785-2264-4c24-8ea0-7554904ef7c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explainable AI for Population Mental Well-being Surveillance Using Community Health Survey 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":"Mental Health, Data Mining, Pattern Analysis, Depression, Pain, Stress","lastPublishedDoi":"10.21203/rs.3.rs-8670939/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8670939/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Population mental well-being surveillance increasingly relies on machine learning methods. However, most existing studies focus on predictive accuracy using black-box models, offering limited transparency and producing findings that are difficult to translate into public health action. This study proposes an explainable AI framework for identifying high-confidence, human-readable patterns of mental well-being outcomes using the Canadian Community Health Survey 2019-2020 data. We analysed 108252 anonymised survey records with approximately 50 attributes capturing demographics, chronic conditions, lifestyle behaviours, and psychosocial factors. After removing non-informative attributes and consolidating sparse response categories, we trained interpretable C4.5 decision tree models for four outcomes: pain status, stress level, work stress, and the Health Utility Index. The proposed approach achieved strong predictive performance across outcomes, with accuracies of 87.0\\% (pain), 82.1\\% (stress), 95.76\\% (work stress), and 82.3\\% (functional health). 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