Towards Sustainable Health: A Time Series Analysis of Health-Related Sustainable Development Goal Indicators (SDG 3, 6) for Bangladesh Using ARIMA and Machine Learning Models

Towards Sustainable Health: A Time Series Analysis of Health-Related Sustainable Development Goal Indicators (SDG 3, 6) for Bangladesh Using ARIMA and Machine Learning Models | 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 Towards Sustainable Health: A Time Series Analysis of Health-Related Sustainable Development Goal Indicators (SDG 3, 6) for Bangladesh Using ARIMA and Machine Learning Models Pratyay Hasan, Tazdin Delwar Khan, Minhajul Abedin, Mohammad Emdadul Haque This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6383497/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Introduction Bangladesh’s progress toward the Sustainable Development Goals (SDGs) remains closely tied to health and environmental outcomes, particularly SDG 3 (Good Health and Well-being) and SDG 6 (Clean Water and Sanitation). Despite substantial advancements in recent decades, persistent challenges threaten continued progress. This study forecasts key health and environmental indicators to assess Bangladesh’s trajectory toward the 2025 and 2030 targets, providing evidence to guide policy and action. Methods This secondary data-based observational study analyzed yearly estimates (2000–2024) of 14 SDG indicators for Bangladesh. Time series analyses were performed using ARIMA models and machine learning techniques (Linear Regression, Random Forest, XGBoost). Model performances were evaluated using mean absolute error (MAE), root mean squared error (RMSE), and mean absolute scaled error (MASE) between ARIMA and ML models and Diebold Mariano Test (α = 0.05) was used for model comparisons among the machine learning models. Forecasts were compared with SDG targets for 2025 and 2030. Data were obtained from the Sustainable Development Report and the Bangladesh SDG Tracker. Results Based on forecasted trends, indicators fall into three categories. Good: Targets (2025 and 2030) achieved or on track, requiring only continued efforts (MMR, under-5 mortality). Fair: Slightly behind but achievable with minor adjustments (neonatal mortality, vaccination, safe water, adolescent fertility, water consumption). Bad: Persistently poor with no improvement, needing urgent action (NCD mortality, road traffic mortality, sanitation, handwashing, well-being, water quality). Machine learning models corroborate these findings, emphasizing the urgency and want of further working in these areas. Conclusion The comparative analysis reveals that no single model is universally superior; Linear Regression excels for strong linear trends, ARIMA for well-structured time series, and XGBoost for volatile, non-linear indicators, providing an evidence-based framework for optimal model selection in SDG forecasting. Bangladesh is on track to achieve some SDG 3 and 6 targets, but critical gaps remain in non-communicable diseases, road safety, adolescent health, sanitation, and water quality. Implementation of strategically planned policy and continuous monitoring are essential to overcome the challenges and achieve the SDG goals by 2030. Sustainable Development Goals SDG Indicators Health ARIMA Machine Learning Time Series Analysis Forecasting Predictive Analysis Bangladesh Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Sep, 2025 Reviewers agreed at journal 26 Sep, 2025 Reviewers invited by journal 24 Sep, 2025 Submission checks completed at journal 19 Sep, 2025 First submitted to journal 18 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. 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-6383497","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521538190,"identity":"63e6619c-5164-49ce-a2b4-a6014fd9c658","order_by":0,"name":"Pratyay 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Models\u003c/p\u003e","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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sustainable Development Goals, SDG, Indicators, Health, ARIMA, Machine Learning, Time Series Analysis, Forecasting, Predictive Analysis, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-6383497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6383497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntroduction\u003c/p\u003e\u003cp\u003eBangladesh\u0026rsquo;s progress toward the Sustainable Development Goals (SDGs) remains closely tied to health and environmental outcomes, particularly SDG 3 (Good Health and Well-being) and SDG 6 (Clean Water and Sanitation). Despite substantial advancements in recent decades, persistent challenges threaten continued progress. This study forecasts key health and environmental indicators to assess Bangladesh\u0026rsquo;s trajectory toward the 2025 and 2030 targets, providing evidence to guide policy and action.\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eThis secondary data-based observational study analyzed yearly estimates (2000\u0026ndash;2024) of 14 SDG indicators for Bangladesh. Time series analyses were performed using ARIMA models and machine learning techniques (Linear Regression, Random Forest, XGBoost). Model performances were evaluated using mean absolute error (MAE), root mean squared error (RMSE), and mean absolute scaled error (MASE) between ARIMA and ML models and Diebold Mariano Test (α\u0026thinsp;=\u0026thinsp;0.05) was used for model comparisons among the machine learning models. Forecasts were compared with SDG targets for 2025 and 2030. Data were obtained from the Sustainable Development Report and the Bangladesh SDG Tracker.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eBased on forecasted trends, indicators fall into three categories. Good: Targets (2025 and 2030) achieved or on track, requiring only continued efforts (MMR, under-5 mortality). Fair: Slightly behind but achievable with minor adjustments (neonatal mortality, vaccination, safe water, adolescent fertility, water consumption). Bad: Persistently poor with no improvement, needing urgent action (NCD mortality, road traffic mortality, sanitation, handwashing, well-being, water quality). Machine learning models corroborate these findings, emphasizing the urgency and want of further working in these areas.\u003c/p\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003cp\u003eThe comparative analysis reveals that no single model is universally superior; Linear Regression excels for strong linear trends, ARIMA for well-structured time series, and XGBoost for volatile, non-linear indicators, providing an evidence-based framework for optimal model selection in SDG forecasting. Bangladesh is on track to achieve some SDG 3 and 6 targets, but critical gaps remain in non-communicable diseases, road safety, adolescent health, sanitation, and water quality. Implementation of strategically planned policy and continuous monitoring are essential to overcome the challenges and achieve the SDG goals by 2030.\u003c/p\u003e","manuscriptTitle":"Towards Sustainable Health: A Time Series Analysis of Health-Related Sustainable Development Goal Indicators (SDG 3, 6) for Bangladesh Using ARIMA and Machine Learning Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 12:54:38","doi":"10.21203/rs.3.rs-6383497/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-09-27T15:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304442848834114398441401644557785109785","date":"2025-09-27T01:55:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-25T01:46:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-20T00:42:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-18T14:23:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"49e32eec-e18e-4506-ae7d-6665880b58c6","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-07T12:54:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-07 12:54:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6383497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6383497","identity":"rs-6383497","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}