Predicting the Level of Anemia among Ethiopian Neonatal Using Ensemble Machine Learning Algorithms

Predicting the Level of Anemia among Ethiopian Neonatal Using Ensemble Machine Learning Algorithms | 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 Predicting the Level of Anemia among Ethiopian Neonatal Using Ensemble Machine Learning Algorithms Yihun Tewachew, Dinkayehu Dagneb, Misganaw Andualem, Yohannes Mekuriaw This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8548736/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Anemia occurs when the body's physiological demands are not met by the quantity of red blood cells, reducing their oxygen-carrying capacity. According to the World Health Organization, neonatal anemia is classified as mild (Hb 10–13.9 g/dL), moderate (Hb 7–9.9 g/dL), normal (Hb 14–24 g/dL), and severe (Hb < 7 g/dL), and is influenced by socioeconomic and demographic factors. Previous studies did not generate actionable rules for policymakers, design or deploy artifacts, or construct multi-class predictive models for neonatal women's anemia based on these factors using machine learning. This study develops a predictive model and prototype for neonatal women's anemia level using data from the Ethiopian Demographic Health Survey (2005–2016). Data preprocessing ensured high-quality input suitable for machine learning. Following a design science research strategy, four experiments were conducted on 42,376 instances with 22 features, splitting data into training and testing sets (80/20) and applying Random Forest, XGBoost, Decision Tree, and CatBoost algorithms, achieving accuracies of 98.37%, 98.22%, 97.92%, and 74.43%, respectively. Random Forest was selected as the best algorithm based on objective and subjective evaluations. Feature importance analysis identified key determinants: age, region, contraceptive use and intention, body mass index, diarrhea, respondent weight, husband’s occupation, cooking fuel type, literacy, wealth index, women’s education, and place of residence. These factors guided artifact design using a Flask framework and deployment on PythonAnywhere cloud platform. Subjective evaluation indicated 88% user acceptance. The model is not integrated with a knowledge-based system; future research should integrate it to develop an intelligent system for automated prediction of neonatal women’s anemia levels. Ensemble Machine Learning Anemia level Predictive Model Neonatal Women Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 May, 2026 Reviews received at journal 25 Apr, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers invited by journal 05 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Editor invited by journal 14 Jan, 2026 Submission checks completed at journal 13 Jan, 2026 First submitted to journal 13 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. 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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-8548736","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586365980,"identity":"aeec3ff9-eb3b-4809-89d6-1b2b43881cac","order_by":0,"name":"Yihun Tewachew","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYDACZh4YC8QwsJEDMQ88IKglAaalIM0YrCUBrzUoWj4cTmxggAngAAbHeY9JF/6wSexvP3vwMY8Bc/r8sMMPgbbYyek24NBymC9NekZCWuKMM3nJxjwGbLkbb6cZALUkG5sdwKWFx0yaJ+FwbsMNICPHgCd34+wEkJYDidvwa/mfO/8Gj/nvHAOJdMPZ6R+I0XIgdwPQFuYcA4MEeaBdeLVIHuYxtuZJS67feCbHWPqPQYLhBumcggMJBrj9wnf+jOFtHhs7Y7njZww/zvjzX15+dvrmDx8q7ORwaVHAEDcAixhgVw4C8g2ERUbBKBgFo2CkAwBKPF+w9kuIygAAAABJRU5ErkJggg==","orcid":"","institution":"University of Gondar","correspondingAuthor":true,"prefix":"","firstName":"Yihun","middleName":"","lastName":"Tewachew","suffix":""},{"id":586365981,"identity":"9ffb1800-e637-4ec6-aad0-4ceca95e7ed9","order_by":1,"name":"Dinkayehu Dagneb","email":"","orcid":"","institution":"University of Kabridahar","correspondingAuthor":false,"prefix":"","firstName":"Dinkayehu","middleName":"","lastName":"Dagneb","suffix":""},{"id":586365982,"identity":"53f0e9f1-dea4-42ff-94dc-ab15ef30c0d7","order_by":2,"name":"Misganaw Andualem","email":"","orcid":"","institution":"University of Gondar","correspondingAuthor":false,"prefix":"","firstName":"Misganaw","middleName":"","lastName":"Andualem","suffix":""},{"id":586365983,"identity":"876678e7-694d-4a4e-93cc-b118ab5985a3","order_by":3,"name":"Yohannes Mekuriaw","email":"","orcid":"","institution":"University of Gondar","correspondingAuthor":false,"prefix":"","firstName":"Yohannes","middleName":"","lastName":"Mekuriaw","suffix":""}],"badges":[],"createdAt":"2026-01-08 08:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8548736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8548736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102178893,"identity":"f051139b-bbc1-4a41-b271-855bb62b8d49","added_by":"auto","created_at":"2026-02-09 06:56:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1082258,"visible":true,"origin":"","legend":"","description":"","filename":"PredictingtheLevelofAnemiaamongEthiopianNeonatalUsingEnsembleMachineLearningAlgorithms.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8548736/v1_covered_a99cb656-9856-439a-8c33-c68819246fdd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting the Level of Anemia among Ethiopian Neonatal Using Ensemble Machine Learning Algorithms","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-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ensemble Machine Learning, Anemia level, Predictive Model, Neonatal Women","lastPublishedDoi":"10.21203/rs.3.rs-8548736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8548736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAnemia occurs when the body's physiological demands are not met by the quantity of red blood cells, reducing their oxygen-carrying capacity. According to the World Health Organization, neonatal anemia is classified as mild (Hb 10\u0026ndash;13.9 g/dL), moderate (Hb 7\u0026ndash;9.9 g/dL), normal (Hb 14\u0026ndash;24 g/dL), and severe (Hb\u0026thinsp;\u0026lt;\u0026thinsp;7 g/dL), and is influenced by socioeconomic and demographic factors. Previous studies did not generate actionable rules for policymakers, design or deploy artifacts, or construct multi-class predictive models for neonatal women's anemia based on these factors using machine learning. This study develops a predictive model and prototype for neonatal women's anemia level using data from the Ethiopian Demographic Health Survey (2005\u0026ndash;2016). Data preprocessing ensured high-quality input suitable for machine learning. Following a design science research strategy, four experiments were conducted on 42,376 instances with 22 features, splitting data into training and testing sets (80/20) and applying Random Forest, XGBoost, Decision Tree, and CatBoost algorithms, achieving accuracies of 98.37%, 98.22%, 97.92%, and 74.43%, respectively. Random Forest was selected as the best algorithm based on objective and subjective evaluations. Feature importance analysis identified key determinants: age, region, contraceptive use and intention, body mass index, diarrhea, respondent weight, husband\u0026rsquo;s occupation, cooking fuel type, literacy, wealth index, women\u0026rsquo;s education, and place of residence. These factors guided artifact design using a Flask framework and deployment on PythonAnywhere cloud platform. Subjective evaluation indicated 88% user acceptance. The model is not integrated with a knowledge-based system; future research should integrate it to develop an intelligent system for automated prediction of neonatal women\u0026rsquo;s anemia levels.\u003c/p\u003e","manuscriptTitle":"Predicting the Level of Anemia among Ethiopian Neonatal Using Ensemble Machine Learning Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 06:55:10","doi":"10.21203/rs.3.rs-8548736/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-13T11:10:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-26T02:44:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-15T00:18:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235978454517560425110314923646306083654","date":"2026-02-11T09:12:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123290718496203263450614925241144469374","date":"2026-02-05T12:50:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327170111229173028911766819077408976152","date":"2026-02-05T11:27:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236491437912924315195069444149387583608","date":"2026-02-05T08:12:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T07:17:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T06:59:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-14T15:09:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T14:11:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2026-01-13T13:57:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f75b60fe-55f7-4625-b7ed-cbc1912ed114","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-13T11:10:09+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T11:24:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 06:55:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8548736","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8548736","identity":"rs-8548736","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}