Predicting birth asphyxia in newborns via supervised machine learning: A prospective cohort study in Tigray, Ethiopia 2025

Predicting birth asphyxia in newborns via supervised machine learning: A prospective cohort study in Tigray, Ethiopia 2025 | 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 birth asphyxia in newborns via supervised machine learning: A prospective cohort study in Tigray, Ethiopia 2025 Goitom Yemane Welay, Kebede Embaye Gezae, Asia Nesredin, Haftom Temesgen Abebe, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8460279/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 Birth asphyxia, a critical condition characterized by insufficient oxygen supply to a newborn before, during, or after birth, is the second leading cause of neonatal mortality in Ethiopia. It contributes substantially to preventable neonatal morbidity and long-term neurodevelopmental impairment. The burden is especially high in low-resource regions such as Tigray, where healthcare systems have been severely impacted by conflict and limited infrastructure. Supervised machine learning (ML) offers a powerful data-driven solution to support clinical decision-making to early and accurately predict newborns at-risk. Methods An institution-based prospective cohort study was conducted among 1014 mothers and their newborns who delivered at four selected hospitals in Tigray (Ayder, Mekelle, Quiha, and Wukro) between February 25 and April 10, 2025. A systematic random sampling technique was used to recruit eligible participants. The dataset underwent thorough preprocessing, including handling missing values, one-hot encoding, normalization, a hybrid feature selection approach, and class balancing. Seven ML models—logistic regression, support vector machine, decision tree, random forest (RF), Naive Bayes, k-nearest neighbors, and extreme gradient boosting were trained and evaluated. Model performance was assessed in terms of accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (AUC) with 95% confidence intervals. Shapley additive explanations (SHAPs) were employed for model interpretability; and were validated across cross-validation folds. Results Of the 1014 neonates included, 195 (19.2%) were diagnosed with birth asphyxia on the basis of APGAR scores and physician confirmation. The random forest classifier achieved the best performance, with an AUC of 0.99 (95% CI: 0.98–1.00) and a Brier score of 0.0099 (95% CI: 0.008–0.012). SHAP analysis revealed that fetal heart rate (38.6%), birth weight (11.2%), malpresentation (8.1%), hypothermia (7.7%), referral status (7.5%), and prolonged labor (6.5%) collectively contributed to 79.6% of the model’s predictive capacity, which was consistent across folds (standard deviation of SHAP values < 0.02). Conclusion The RF model demonstrated excellent performance in predicting birth asphyxia and offered strong interpretability. Nearly 80% of the model's predictive power was explained by a small number of clinically actionable variables. These findings support the integration of interpretable machine learning tools into routine labor management to reduce birth asphyxia. Birth asphyxia Ethiopia Neonate Predictive modeling Supervised machine learning Tigray Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 30 Jan, 2026 Editor assigned by journal 27 Jan, 2026 Editor invited by journal 05 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 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-8460279","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582887732,"identity":"05ece832-7204-408a-9c39-618d18867cf1","order_by":0,"name":"Goitom Yemane 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study in Tigray, Ethiopia 2025","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":"Birth asphyxia, Ethiopia, Neonate, Predictive modeling, Supervised machine learning, Tigray","lastPublishedDoi":"10.21203/rs.3.rs-8460279/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8460279/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBirth asphyxia, a critical condition characterized by insufficient oxygen supply to a newborn before, during, or after birth, is the second leading cause of neonatal mortality in Ethiopia. It contributes substantially to preventable neonatal morbidity and long-term neurodevelopmental impairment. The burden is especially high in low-resource regions such as Tigray, where healthcare systems have been severely impacted by conflict and limited infrastructure. Supervised machine learning (ML) offers a powerful data-driven solution to support clinical decision-making to early and accurately predict newborns at-risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAn institution-based prospective cohort study was conducted among 1014 mothers and their newborns who delivered at four selected hospitals in Tigray (Ayder, Mekelle, Quiha, and Wukro) between February 25 and April 10, 2025. A systematic random sampling technique was used to recruit eligible participants. The dataset underwent thorough preprocessing, including handling missing values, one-hot encoding, normalization, a hybrid feature selection approach, and class balancing. Seven ML models\u0026mdash;logistic regression, support vector machine, decision tree, random forest (RF), Naive Bayes, k-nearest neighbors, and extreme gradient boosting were trained and evaluated. Model performance was assessed in terms of accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (AUC) with 95% confidence intervals. Shapley additive explanations (SHAPs) were employed for model interpretability; and were validated across cross-validation folds.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOf the 1014 neonates included, 195 (19.2%) were diagnosed with birth asphyxia on the basis of APGAR scores and physician confirmation. The random forest classifier achieved the best performance, with an AUC of 0.99 (95% CI: 0.98\u0026ndash;1.00) and a Brier score of 0.0099 (95% CI: 0.008\u0026ndash;0.012). SHAP analysis revealed that fetal heart rate (38.6%), birth weight (11.2%), malpresentation (8.1%), hypothermia (7.7%), referral status (7.5%), and prolonged labor (6.5%) collectively contributed to 79.6% of the model\u0026rsquo;s predictive capacity, which was consistent across folds (standard deviation of SHAP values\u0026thinsp;\u0026lt;\u0026thinsp;0.02).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe RF model demonstrated excellent performance in predicting birth asphyxia and offered strong interpretability. Nearly 80% of the model's predictive power was explained by a small number of clinically actionable variables. These findings support the integration of interpretable machine learning tools into routine labor management to reduce birth asphyxia.\u003c/p\u003e","manuscriptTitle":"Predicting birth asphyxia in newborns via supervised machine learning: A prospective cohort study in Tigray, Ethiopia 2025","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 18:20:10","doi":"10.21203/rs.3.rs-8460279/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-22T21:16:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119355883319868369194227860791207269546","date":"2026-02-08T11:02:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165789152938612651276723548652703017625","date":"2026-02-06T08:12:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"87142917348728303057913231616377795426","date":"2026-02-04T07:59:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T07:43:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-27T08:52:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T04:53:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T11:02:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Informatics and Decision Making","date":"2026-01-05T10:43:58+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":"a6a4c111-59bb-4214-a140-17971921d81e","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T18:20:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 18:20:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8460279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8460279","identity":"rs-8460279","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}