Predicting Under Five Mortality in Bihar Through Machine Learning and SDG Metrics

Predicting Under Five Mortality in Bihar Through Machine Learning and SDG Metrics | 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 Under Five Mortality in Bihar Through Machine Learning and SDG Metrics Muskaan Gupta, Sacheendra Shukla, Niraj Kumar Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7176124/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Child mortality is a vital indicator of a nation’s health and development, closely aligned with the Sustainable Development Goals (SDGs). This study investigates the determinants of under-five mortality in Bihar, India, utilizing data from the National Family Health Survey (NFHS-5, 2019–21). A total of 21,040 records of children born to married women were analyzed using 33 predictor variables selected based on their relevance to SDG targets. The research employs a comparative machine learning approach, evaluating the predictive performance of Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Naıve Bayes, and Support Vector Machine (SVM) models. The results reveal that Random Forest and Naıve Bayes models achieved the highest accuracy (98.80% and 98.67%, respectively), with Naıve Bayes attaining perfect recall (100%) and an F1 score of 99.53%, while Random Forest achieved an F1 scoreof 98.73%. Logistic Regression showed moderate performance with 76.61% accuracy, 74.92% precision, and an F1 score of 76.31%. K-Nearest Neighbors(KNN) achieved 84.12% accuracy and 88.56% precision, but had a lower recallof 75.24%. The Support Vector Machine (SVM) model performed well with 86.38% accuracy and a balanced F1 score of 86.46%. AUC-ROC scores ranged from 85.64% (Logistic Regression) to 99.96% (Random Forest), indicating strong model discrimination across the board. These findings underscore the potential of machine learning in identifying key socio-demographic, economic, and health related factors influencing child survival. The study provides valuable insights for policymakers aiming to reduce child mortality and achieve SDG targets inBihar. Child mortality NFHS-5 Machine learning Random Forest Naïve Bayes Logistic Regression K-Nearest Neighbors Support Vector Machine Sustainable Development Goals (SDGs) AUC-ROC Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7176124","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496845159,"identity":"8d3e00da-bc4f-43ff-825b-bb85eba33c9b","order_by":0,"name":"Muskaan Gupta","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Muskaan","middleName":"","lastName":"Gupta","suffix":""},{"id":496845160,"identity":"7f40a60d-1084-441d-b76a-b9ba95e52e0c","order_by":1,"name":"Sacheendra Shukla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYFACHgbGhgIGOX4g8wBY4ABRWgwYjCUbgIoPkKIlccMBmGpCWuT7zx6TnGFgx7j5+OnEwx/bGOT4biTg12JwIy9NcoNBMrPZmdwNBw62AV1IUIsEj5nkAwNmNrMDEC2JGwhpke8/A9JSz2Pc/xaspZ6gFoYDOWZAhx2WMJCA2JJgQNgvOcaWMwyOG0jcANpy5pyE4cwzDwg6zPBmT0V1fX9/7uYPFWU28nzHCTkMBTCySZCiHAz+kKxjFIyCUTAKRgAAAKENTiMeNaT2AAAAAElFTkSuQmCC","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Sacheendra","middleName":"","lastName":"Shukla","suffix":""},{"id":496845161,"identity":"6fd2ba3f-db89-49e0-89ba-4ee52555837e","order_by":2,"name":"Niraj Kumar Singh","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Niraj","middleName":"Kumar","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2025-07-21 10:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7176124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7176124/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90003391,"identity":"170f18b5-64c3-4c18-9156-06ce5d934853","added_by":"auto","created_at":"2025-08-27 09:09:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":500826,"visible":true,"origin":"","legend":"","description":"","filename":"RESEARCHPAPER3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7176124/v1_covered_bbe38ada-77b8-43b2-8a4d-6aad438ba7c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Under Five Mortality in Bihar Through Machine Learning and SDG Metrics","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Child mortality; NFHS-5, Machine learning, Random Forest, Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Sustainable Development Goals (SDGs), AUC-ROC","lastPublishedDoi":"10.21203/rs.3.rs-7176124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7176124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Child mortality is a vital indicator of a nation’s health and development, closely aligned with the Sustainable Development Goals (SDGs). This study investigates the determinants of under-five mortality in Bihar, India, utilizing data from the National Family Health Survey (NFHS-5, 2019–21). A total of 21,040 records of children born to married women were analyzed using 33 predictor variables selected based on their relevance to SDG targets. The research employs a comparative machine learning approach, evaluating the predictive performance of Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Naıve Bayes, and Support Vector Machine (SVM) models. The results reveal that Random Forest and Naıve Bayes models achieved the highest accuracy (98.80% and 98.67%, respectively), with Naıve Bayes attaining perfect recall (100%) and an F1 score of 99.53%, while Random Forest achieved an F1 scoreof 98.73%. Logistic Regression showed moderate performance with 76.61% accuracy, 74.92% precision, and an F1 score of 76.31%. 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