Stunting, Wasting, and Underweight among Under-five Children: A Multivariate Probit and ML Analysis

Stunting, Wasting, and Underweight among Under-five Children: A Multivariate Probit and ML Analysis | 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 Stunting, Wasting, and Underweight among Under-five Children: A Multivariate Probit and ML Analysis Mansi Sharma, Rohan Gopal Kulkarni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6230681/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 In this paper, we examine the factors influencing child malnutrition in India by measuring stunting, wasting, and underweight, using data from the Indian Demographic and Health Survey (DHS). We estimate a multivariate probit model to analyze the determinants of these outcomes and also account for shared unobserved factors such as unobserved household- or community-level influences. In addition, instead of dividing India into six predefined regions, we apply a machine learning-based clustering algorithm to segment the data into distinct groups. We improve computational efficiency and enhance cluster interpretability by defining clusters using the top 10 features given by the Random Forest classifier, which captures the strong predictors of child malnutrition. Our findings from multivariate probit reveal interesting interrelationships among the three forms of malnutrition: a strong positive correlation between the unobserved factors influencing underweight and stunting, the same for wasting and underweight, and a negative correlation between stunting and wasting. We also find that maternal characteristics, for example, the mother’s educational attainment, the mother’s age at first birth, and the mother’s age at cohabitation, are strong predictors of child health outcomes. Finally, the clustering analysis indicates the common high-risk groups within regions that conventional regional analyses might overlook, offering valuable insights for targeted policy interventions. Health sciences/Health care/Nutrition Health sciences/Health care 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. 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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-6230681","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":439694650,"identity":"aad79be6-9dc9-421a-8717-524794ed58ab","order_by":0,"name":"Mansi Sharma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYLCCBDACgg9gkgcqBhXEq4VxBoMBkVpgssw8xGjRbT97+MMDhro8/tmnEz/b/PkjZy6Re+zDh4o0Bn72HANsWszO5KVJJDAcLpY4l7tZOrfNwNhyRl7yzBlnchgke95g13IgxwzogAOJDWd4N0jnNhgkbriRY8zM21bBYHADhy3n3xh/SGCoS5x/hnfzb4s/SFrscWkBigMdxpy44QzvNmkGNriWHAYDCVxa3phJJBgcTtwI1GLZ22ZsbHDmXTLjjDNpPBJnnhVgd1iO8ccfFXWJ84AOu/Hjj5ycwfHcwwwfKpLl+NuTN2ANZTBAcYBAApjiwa0cA/AfIEHxKBgFo2AUjAQAAC5cZOXxrz5iAAAAAElFTkSuQmCC","orcid":"","institution":"Stony Brook University","correspondingAuthor":true,"prefix":"","firstName":"Mansi","middleName":"","lastName":"Sharma","suffix":""},{"id":439694652,"identity":"3d1f5d1b-f90e-44c1-ac3c-b37410388ac4","order_by":1,"name":"Rohan Gopal Kulkarni","email":"","orcid":"","institution":"Stony Brook University","correspondingAuthor":false,"prefix":"","firstName":"Rohan","middleName":"Gopal","lastName":"Kulkarni","suffix":""}],"badges":[],"createdAt":"2025-03-15 05:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6230681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6230681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81253742,"identity":"26193fa9-0751-4cb9-b5cd-167bcaf31c61","added_by":"auto","created_at":"2025-04-24 04:01:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":290801,"visible":true,"origin":"","legend":"","description":"","filename":"StuntingWastingandUnderweightamongUnderfiveChildrenAMultivariateProbitandMLAnalysis.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6230681/v1_covered_e9e0d66a-06c3-4230-86ed-58e3bf921cd6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stunting, Wasting, and Underweight among Under-five Children: A Multivariate Probit and ML Analysis","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6230681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6230681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we examine the factors influencing child malnutrition in India by measuring stunting, wasting, and underweight, using data from the Indian Demographic and Health Survey (DHS). 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