Machine Learning Prediction of Iron Supplement Utilization Among Pregnant Women in Somaliland: Evidence from the Somaliland Demographic and Health Survey 2020

Machine Learning Prediction of Iron Supplement Utilization Among Pregnant Women in Somaliland: Evidence from the Somaliland Demographic and Health Survey 2020 | 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 Prediction of Iron Supplement Utilization Among Pregnant Women in Somaliland: Evidence from the Somaliland Demographic and Health Survey 2020 Hamda Jama Yousuf, Abdiasis Adem Omar, Suhur A. Ahmed, Ahmed Abdi Omar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9082298/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Iron supplementation during pregnancy is a key public health intervention for preventing maternal anemia and improving maternal and neonatal outcomes. This study aimed to identify the determinants and predict iron supplement utilization among pregnant women in Somaliland using machine learning techniques. Methods This study used data from the 2020 Somaliland Demographic and Health Survey (SLDHS), a nationally representative cross-sectional survey. The outcome variable was iron supplement utilization during pregnancy. Descriptive statistics and bivariate analyses were conducted to examine associations between independent variables and iron supplement use. Several supervised machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, and Extreme Gradient Boosting (XGBoost), were applied to predict iron supplement utilization. The dataset was split into training (80%) and testing (20%) subsets, and model performance was evaluated using accuracy, precision, recall, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Results The prevalence of iron supplement utilization among pregnant women was 27%, indicating low coverage in the study population. Bivariate analysis revealed that region, educational level, wealth index, distance to a health facility, husband’s employment status, antenatal care (ANC) visits, and media exposure were significantly associated with iron supplement use (p < 0.05). Among the machine learning models, Support Vector Machine achieved the highest accuracy (82.6%), followed by Logistic Regression (81.7%) and Random Forest (80.6%). Logistic Regression (AUC = 0.853), Random Forest (AUC = 0.852), and SVM (AUC = 0.850) demonstrated the strongest discriminatory performance. Feature importance analysis indicated that ANC utilization, husband’s employment status, media exposure, and distance to health facilities were the most influential predictors of iron supplement utilization. Conclusion Iron supplementation during pregnancy remains substantially low in Somaliland. Maternal healthcare utilization, socioeconomic status, and access to health information play important roles in determining iron supplement use. Machine learning approaches demonstrated strong predictive performance in identifying key determinants of iron supplementation. Strengthening antenatal care services, improving maternal health education through mass media, and addressing geographic barriers to healthcare access may significantly improve iron supplementation coverage and maternal nutrition in Somaliland. Iron supplementation pregnancy maternal health machine learning antenatal care Somaliland Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor invited by journal 16 Mar, 2026 Editor assigned by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 10 Mar, 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-9082298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":604914779,"identity":"db96191d-3eac-4c50-8b97-57e900074bb1","order_by":0,"name":"Hamda Jama Yousuf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAyCWYKiAcJhJ0HKGZC2MbaRoMWc/nXjj5zwbu/72s4c/F1QwyJn3L8CvxbInd7Nl77a05Bln8tKkZ5xhMJa58YCAww7kbpPg3XY4meFAjhkzbxtD4gyJAwS0nH+7TfLvnMPJ8uffGH8mTsuN3G3SvA2H7Qxu5BhIg7XwNxDS8naztcyxtATDG2/MpHnOSBhLSODXAXRY7sabb2ps7OXO5xh/5qmwkZPgJ+AwGEiEugZohUQCcVrsEUxibRkFo2AUjIIRAwCvNkUWwhBN+QAAAABJRU5ErkJggg==","orcid":"","institution":"Ogaansho Research and Consultancy Centre","correspondingAuthor":true,"prefix":"","firstName":"Hamda","middleName":"Jama","lastName":"Yousuf","suffix":""},{"id":604914780,"identity":"f07ae4f0-7cec-465b-a2a4-c22dc9296f31","order_by":1,"name":"Abdiasis Adem Omar","email":"","orcid":"","institution":"Ogaansho Research and Consultancy Centre","correspondingAuthor":false,"prefix":"","firstName":"Abdiasis","middleName":"Adem","lastName":"Omar","suffix":""},{"id":604914781,"identity":"a41598a2-0889-4a43-b5ff-e66c047e015c","order_by":2,"name":"Suhur A. 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This study aimed to identify the determinants and predict iron supplement utilization among pregnant women in Somaliland using machine learning techniques.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study used data from the 2020 Somaliland Demographic and Health Survey (SLDHS), a nationally representative cross-sectional survey. The outcome variable was iron supplement utilization during pregnancy. Descriptive statistics and bivariate analyses were conducted to examine associations between independent variables and iron supplement use. Several supervised machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Na\u0026iuml;ve Bayes, and Extreme Gradient Boosting (XGBoost), were applied to predict iron supplement utilization. The dataset was split into training (80%) and testing (20%) subsets, and model performance was evaluated using accuracy, precision, recall, specificity, and area under the receiver operating characteristic curve (AUC-ROC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of iron supplement utilization among pregnant women was 27%, indicating low coverage in the study population. Bivariate analysis revealed that region, educational level, wealth index, distance to a health facility, husband\u0026rsquo;s employment status, antenatal care (ANC) visits, and media exposure were significantly associated with iron supplement use (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among the machine learning models, Support Vector Machine achieved the highest accuracy (82.6%), followed by Logistic Regression (81.7%) and Random Forest (80.6%). Logistic Regression (AUC\u0026thinsp;=\u0026thinsp;0.853), Random Forest (AUC\u0026thinsp;=\u0026thinsp;0.852), and SVM (AUC\u0026thinsp;=\u0026thinsp;0.850) demonstrated the strongest discriminatory performance. Feature importance analysis indicated that ANC utilization, husband\u0026rsquo;s employment status, media exposure, and distance to health facilities were the most influential predictors of iron supplement utilization.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIron supplementation during pregnancy remains substantially low in Somaliland. Maternal healthcare utilization, socioeconomic status, and access to health information play important roles in determining iron supplement use. Machine learning approaches demonstrated strong predictive performance in identifying key determinants of iron supplementation. Strengthening antenatal care services, improving maternal health education through mass media, and addressing geographic barriers to healthcare access may significantly improve iron supplementation coverage and maternal nutrition in Somaliland.\u003c/p\u003e","manuscriptTitle":"Machine Learning Prediction of Iron Supplement Utilization Among Pregnant Women in Somaliland: Evidence from the Somaliland Demographic and Health Survey 2020","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 17:07:03","doi":"10.21203/rs.3.rs-9082298/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-23T15:56:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316067484583942303829283048182946374257","date":"2026-04-14T14:51:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72624160577553125939634018005632834002","date":"2026-04-09T03:47:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176665459499800820685553010751647960999","date":"2026-04-07T18:17:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T16:34:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-16T09:29:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-12T08:12:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-12T08:11:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-03-10T09:37:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"79edc2b0-4dd6-46f5-ae6f-4f876456c430","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:39:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 17:07:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9082298","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9082298","identity":"rs-9082298","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}