Optimizing machine learning models for predicting iron supplementation uptake among pregnant women in Somaliland: insights from the 2020 Somaliland demographic and health survey data

Optimizing machine learning models for predicting iron supplementation uptake among pregnant women in Somaliland: insights from the 2020 Somaliland demographic and 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 Optimizing machine learning models for predicting iron supplementation uptake among pregnant women in Somaliland: insights from the 2020 Somaliland demographic and health survey data Abdifatah Ibrahim Mouse, Omran Salih, Abdisalam Hassan Muse This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8431473/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Iron supplementation during pregnancy is crucial, fulfilling increased demands for placental and fetal development. Despite WHO recommendations and efforts to promote iron intake, uptake remains suboptimal in many regions, including Somaliland, where maternal and child health indicators are poor due to limited healthcare access and nutritional deficiencies. This study aims to identify determinants of iron supplementation to inform targeted interventions. This cross-sectional study utilized data from the 2020 Somaliland Health and Demographic Survey (SLHDS) for a sample of 2,983 pregnant women. Explanatory variables included maternal age, education, employment status, ANC visits, residence type, region, media exposure, and wealth. Supervised machine learning models including, Logistic Regression, Random Forest, XGBoost, LightGBM, Support Vector Machine, and K-Nearest Neighbors were employed to predict iron supplementation uptake. Performance was evaluated using accuracy, precision, recall, F1-score, and AUROC. Overall, 28.83% of pregnant women reported taking iron supplements. Bivariate analysis revealed significant associations (p < 0.05) between iron supplementation and maternal age (χ2 = 15.00, p = 0.020), educational level (χ2 = 117.3, p < 0.001), employment status (χ2 = 5.5, p = 0.019), ANC visits (χ2 = 259.5, p < 0.001), region (χ2 = 103.5, p < 0.001), media exposure (χ2 = 22.3, p < 0.001), and wealth quintile (χ2 = 261.1, p < 0.001). The Random Forest model demonstrated the best performance, achieving an accuracy of 0.785 and an AUROC of 0.81. Iron supplementation uptake in Somaliland remains suboptimal, with only 28.83% reporting adherence, underscoring a critical need for enhanced interventions. The Random Forest model highlighted key predictors of iron supplementation uptake: wealth status, region, antenatal care visits, and maternal age. These findings emphasize the importance of socioeconomic factors, geographical location, and access to healthcare services in influencing iron supplementation behaviors. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Iron Supplementation Pregnant Women Machine Learning Random Forest Somaliland Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 11 May, 2026 Editor assigned by journal 05 May, 2026 Editor invited by journal 23 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 16 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-8431473","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596010593,"identity":"8df1e533-95a3-4029-a5c2-66c03d5a80b8","order_by":0,"name":"Abdifatah Ibrahim Mouse","email":"data:image/png;base64,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","orcid":"","institution":"Amoud University","correspondingAuthor":true,"prefix":"","firstName":"Abdifatah","middleName":"Ibrahim","lastName":"Mouse","suffix":""},{"id":596010594,"identity":"9ce16aa3-fb74-4c11-af43-6a9d2921c7c7","order_by":1,"name":"Omran Salih","email":"","orcid":"","institution":"Durban University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Omran","middleName":"","lastName":"Salih","suffix":""},{"id":596010595,"identity":"39697aba-86ca-4d27-ae9e-f114a8f1ab14","order_by":2,"name":"Abdisalam Hassan Muse","email":"","orcid":"","institution":"Amoud University","correspondingAuthor":false,"prefix":"","firstName":"Abdisalam","middleName":"Hassan","lastName":"Muse","suffix":""}],"badges":[],"createdAt":"2025-12-23 07:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8431473/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8431473/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104815966,"identity":"c7bd3152-516a-4e0f-b340-18e22e02af46","added_by":"auto","created_at":"2026-03-17 13:27:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":554209,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptMachinelearningbasedidentificationofpredictorsofironsupplementuseamongpregnantwomeninSomaliland.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8431473/v1_covered_eff299d4-9d98-47d1-9ed8-3134177c06a2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing machine learning models for predicting iron supplementation uptake among pregnant women in Somaliland: insights from the 2020 Somaliland demographic and health survey data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Iron Supplementation, Pregnant Women, Machine Learning, Random Forest, Somaliland","lastPublishedDoi":"10.21203/rs.3.rs-8431473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8431473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIron supplementation during pregnancy is crucial, fulfilling increased demands for placental and fetal development. Despite WHO recommendations and efforts to promote iron intake, uptake remains suboptimal in many regions, including Somaliland, where maternal and child health indicators are poor due to limited healthcare access and nutritional deficiencies. This study aims to identify determinants of iron supplementation to inform targeted interventions. This cross-sectional study utilized data from the 2020 Somaliland Health and Demographic Survey (SLHDS) for a sample of 2,983 pregnant women. Explanatory variables included maternal age, education, employment status, ANC visits, residence type, region, media exposure, and wealth. Supervised machine learning models including, Logistic Regression, Random Forest, XGBoost, LightGBM, Support Vector Machine, and K-Nearest Neighbors were employed to predict iron supplementation uptake. Performance was evaluated using accuracy, precision, recall, F1-score, and AUROC. Overall, 28.83% of pregnant women reported taking iron supplements. Bivariate analysis revealed significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between iron supplementation and maternal age (χ2\u0026thinsp;=\u0026thinsp;15.00, p\u0026thinsp;=\u0026thinsp;0.020), educational level (χ2\u0026thinsp;=\u0026thinsp;117.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), employment status (χ2\u0026thinsp;=\u0026thinsp;5.5, p\u0026thinsp;=\u0026thinsp;0.019), ANC visits (χ2\u0026thinsp;=\u0026thinsp;259.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), region (χ2\u0026thinsp;=\u0026thinsp;103.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), media exposure (χ2\u0026thinsp;=\u0026thinsp;22.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and wealth quintile (χ2\u0026thinsp;=\u0026thinsp;261.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Random Forest model demonstrated the best performance, achieving an accuracy of 0.785 and an AUROC of 0.81. Iron supplementation uptake in Somaliland remains suboptimal, with only 28.83% reporting adherence, underscoring a critical need for enhanced interventions. The Random Forest model highlighted key predictors of iron supplementation uptake: wealth status, region, antenatal care visits, and maternal age. These findings emphasize the importance of socioeconomic factors, geographical location, and access to healthcare services in influencing iron supplementation behaviors.\u003c/p\u003e","manuscriptTitle":"Optimizing machine learning models for predicting iron supplementation uptake among pregnant women in Somaliland: insights from the 2020 Somaliland demographic and health survey data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 13:24:22","doi":"10.21203/rs.3.rs-8431473/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"273970214939086309899215949834369756390","date":"2026-05-11T16:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-11T16:32:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T11:36:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-23T07:12:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T10:43:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-16T10:33:10+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"6d1c985f-2e8f-436f-b71d-342cbea960e2","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"273970214939086309899215949834369756390","date":"2026-05-11T16:35:05+00:00","index":84,"fulltext":""},{"type":"reviewersInvited","content":"10","date":"2026-05-11T16:32:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-05T11:36:03+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63417459,"name":"Health sciences/Diseases"},{"id":63417460,"name":"Health sciences/Health care"},{"id":63417461,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-11T16:40:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 13:24:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8431473","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8431473","identity":"rs-8431473","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}