Predicting Early Neonatal Mortality using Machine Learning Models

Predicting Early Neonatal Mortality using 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 Predicting Early Neonatal Mortality using Machine Learning Models SULAIMAN SALIM AL MASHRAFI, Laleh Tafakori, Mali Abdollahian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7016070/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted 12 You are reading this latest preprint version Abstract Background Neonatal mortality is a major issue in global health and is included in the Sustainable Development Goals (SDGs). Early neonatal deaths account for 47% of under-five mortality. Developing a dependable model to predict early neonatal mortality and recognise its related risk factors is essential for child survival and enhancing children's health outcomes. We utilised various machine learning models to predict early neonatal mortality using a comprehensive secondary dataset from Oman. Methods Ten different machine learning models were used in three distinct setups: using the original local dataset, applying the data-driven approach represented bySynthetic Minority Over-Sampling Technique (SMOTE) to address the imbalanced distribution, and implementing an algorithm-driven approach via cost-sensitive classification. The goal was to predict early neonatal mortality and identify its associated risk factors. A total of 2,940 de-identified local records on newborn deaths were categorised into early deaths (0–6 days) and late deaths (7–27 days) for model training and testing using a 10-fold cross-validation. Model performance was evaluated based on accuracy, sensitivity, precision, F1-score, and Area Under the Curve (AUC). Given the issue of an imbalanced dataset, AUC was pivotal in evaluating the models. Results The analysis revealed that 71.6% of the deaths occurred during the early neonatal period (0–6 days). Logistic regression (LR) and Linear Discriminant Analysis (LDA) were the top-performing models in two out of the three scenarios, with LR achieving an AUC between 0.7085 and 0.7248, and LDA between 0.7057 and 0.7229. The APGAR score at 5 minutes was identified as the most significant predictor of early neonatal mortality. Conclusion This study is one of the first to train and evaluate multiple machine learning algorithms under three different scenarios to predict early neonatal mortality and identify associated risk factors using real data from Oman. The results indicate that Logistic Regression and Linear Discriminant Analysis performed the best based on their AUC scores. The findings have the potential to inform clinical decision-making and prompt timely interventions to enhance survival rates. Neonatal mortality early and late neonatal deaths neonatal mortality prediction machine learning Oman Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Nov, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 31 Jul, 2025 Reviews received at journal 31 Jul, 2025 Reviews received at journal 30 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers agreed at journal 22 Jul, 2025 Reviewers invited by journal 22 Jul, 2025 Editor invited by journal 03 Jul, 2025 Editor assigned by journal 02 Jul, 2025 Submission checks completed at journal 02 Jul, 2025 First submitted to journal 01 Jul, 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. 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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-7016070","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489664117,"identity":"aa4ddbe2-b751-447a-b93f-b528eb4793ce","order_by":0,"name":"SULAIMAN SALIM AL MASHRAFI","email":"data:image/png;base64,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","orcid":"","institution":"RMIT University","correspondingAuthor":true,"prefix":"","firstName":"SULAIMAN","middleName":"SALIM AL","lastName":"MASHRAFI","suffix":""},{"id":489664118,"identity":"f7d7d8b6-8c60-4d59-8c9f-e853aacdc033","order_by":1,"name":"Laleh Tafakori","email":"","orcid":"","institution":"RMIT University","correspondingAuthor":false,"prefix":"","firstName":"Laleh","middleName":"","lastName":"Tafakori","suffix":""},{"id":489664119,"identity":"1394940b-af38-471a-854f-551da24336cf","order_by":2,"name":"Mali Abdollahian","email":"","orcid":"","institution":"RMIT University","correspondingAuthor":false,"prefix":"","firstName":"Mali","middleName":"","lastName":"Abdollahian","suffix":""}],"badges":[],"createdAt":"2025-07-01 05:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7016070/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7016070/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25796-1","type":"published","date":"2025-11-29T15:57:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97178650,"identity":"3eb58a3d-7bf2-4604-9e86-51823720d3eb","added_by":"auto","created_at":"2025-12-01 16:12:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":684131,"visible":true,"origin":"","legend":"","description":"","filename":"Suliaman3rdpaperVersion8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7016070/v1_covered_7fefe0cc-0eb4-4ae0-b760-3a39f9d982c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Early Neonatal Mortality using Machine Learning Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Neonatal mortality, early and late neonatal deaths, neonatal mortality prediction, machine learning, Oman","lastPublishedDoi":"10.21203/rs.3.rs-7016070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7016070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eNeonatal mortality is a major issue in global health and is included in the Sustainable Development Goals (SDGs). Early neonatal deaths account for 47% of under-five mortality. Developing a dependable model to predict early neonatal mortality and recognise its related risk factors is essential for child survival and enhancing children's health outcomes. We utilised various machine learning models to predict early neonatal mortality using a comprehensive secondary dataset from Oman.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eTen different machine learning models were used in three distinct setups: using the original local dataset, applying the data-driven approach represented bySynthetic Minority Over-Sampling Technique (SMOTE) to address the imbalanced distribution, and implementing an algorithm-driven approach via cost-sensitive classification. The goal was to predict early neonatal mortality and identify its associated risk factors. A total of 2,940 de-identified local records on newborn deaths were categorised into early deaths (0\u0026ndash;6 days) and late deaths (7\u0026ndash;27 days) for model training and testing using a 10-fold cross-validation. Model performance was evaluated based on accuracy, sensitivity, precision, F1-score, and Area Under the Curve (AUC). Given the issue of an imbalanced dataset, AUC was pivotal in evaluating the models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe analysis revealed that 71.6% of the deaths occurred during the early neonatal period (0\u0026ndash;6 days). Logistic regression (LR) and Linear Discriminant Analysis (LDA) were the top-performing models in two out of the three scenarios, with LR achieving an AUC between 0.7085 and 0.7248, and LDA between 0.7057 and 0.7229. The APGAR score at 5 minutes was identified as the most significant predictor of early neonatal mortality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study is one of the first to train and evaluate multiple machine learning algorithms under three different scenarios to predict early neonatal mortality and identify associated risk factors using real data from Oman. The results indicate that Logistic Regression and Linear Discriminant Analysis performed the best based on their AUC scores. The findings have the potential to inform clinical decision-making and prompt timely interventions to enhance survival rates.\u003c/p\u003e","manuscriptTitle":"Predicting Early Neonatal Mortality using Machine Learning Models","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 18:34:08","doi":"10.21203/rs.3.rs-7016070/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-31T17:21:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-31T05:37:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-30T07:22:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242282441349248954134567817544682879583","date":"2025-07-24T13:27:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128557043709589815085977670907990312274","date":"2025-07-23T09:51:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150756367104384753308491895037900915952","date":"2025-07-22T15:04:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49337012473957830103661861692917116809","date":"2025-07-22T13:14:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-22T12:58:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-03T10:11:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-02T04:18:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-02T04:17:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-07-01T04:58:41+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":"1588f85b-48b1-49bc-809f-35bb7e055620","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:05:13+00:00","versionOfRecord":{"articleIdentity":"rs-7016070","link":"https://doi.org/10.1186/s12889-025-25796-1","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-11-29 15:57:28","publishedOnDateReadable":"November 29th, 2025"},"versionCreatedAt":"2025-07-24 18:34:08","video":"","vorDoi":"10.1186/s12889-025-25796-1","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25796-1","workflowStages":[]},"version":"v1","identity":"rs-7016070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7016070","identity":"rs-7016070","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}