Machine learning for predicting preterm birth: A cross-database analysis on Pernambuco, Brazil cases

Machine learning for predicting preterm birth: A cross-database analysis on Pernambuco, Brazil cases | 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 Machine learning for predicting preterm birth: A cross-database analysis on Pernambuco, Brazil cases Anna Beatriz Silva, Elisson Silva Rocha, Waldemar Brandão Neto, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6711220/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 Preterm birth remains a major public health challenge globally and is the leading cause of death in children under five. This study investigates the predictive capacity of machine learning models for preterm birth using two health datasets from Pernambuco, Brazil: the national-level SINASC and the state-level SIS-MC. The research evaluates the models’ performance under different data balancing techniques—random undersampling and hybrid sampling—and examines the influence of threshold adjustment to optimize clinical decision-making metrics. A cross-database approach was employed to assess the performance of the models across distinct data collection frameworks, considering variations in attribute availability, data completeness, and contextual relevance. Five tree-based classifiers (Decision Tree, Random Forest, XGBoost, CatBoost, and LightGBM) were trained and evaluated using a standardized preprocessing pipeline and hyperparameter optimization. Findings indicate that although models generally achieved higher accuracy for term births, sensitivity for preterm cases remained limited, especially when trained with imbalanced data. The undersampling strategy, when coupled with threshold tuning based on ROC curve analysis, resulted in the most favorable trade-off between sensitivity and positive predictive value (PPV). Cross-database evaluation revealed performance degradation when models trained on one dataset were applied to another, highlighting the influence of data heterogeneity and the importance of local context in model development. This study underscores the necessity for attribute harmonization and transfer learning strategies to improve model adaptability across diverse healthcare settings. Health sciences/Health care/Public health Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology 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-6711220","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":463999270,"identity":"a12117e8-a608-4d93-ae72-39ab457cbe24","order_by":0,"name":"Anna Beatriz Silva","email":"","orcid":"","institution":"Universidade de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"Beatriz","lastName":"Silva","suffix":""},{"id":463999272,"identity":"16bf6384-0eab-4688-86a2-8899696daf76","order_by":1,"name":"Elisson Silva Rocha","email":"","orcid":"","institution":"Universidade de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Elisson","middleName":"Silva","lastName":"Rocha","suffix":""},{"id":463999274,"identity":"81a66c87-22fd-4cc8-9602-17e059a3625b","order_by":2,"name":"Waldemar Brandão Neto","email":"","orcid":"","institution":"Universidade de Pernambuco","correspondingAuthor":false,"prefix":"","firstName":"Waldemar","middleName":"Brandão","lastName":"Neto","suffix":""},{"id":463999277,"identity":"b1f0f639-1dea-4e23-90a7-1c7bf6fdb5da","order_by":3,"name":"Patricia Takako Endo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYLCCBAYJMC3BUAGieAipZ0bWcoZYLTAgwdhGhBZz9vPHPjyosZBnYO89eOPjvMN5/Ay8xz7g02LZk8w8I+GYhGEDz7lky5nbDhdLNvAlz8CnxeBAMjNDYoMEY4NEjpk077bDiRsO8BjjdZjB+cdgLfYN8m+AWuYcTtxPUMsNiC1AxAPU0gC0hYGAFssZj40ZgH5JbuPJMbaccSy9WOIwXzJeLeb8iY8Zf9TU2faznzG88aHGOo+/vfcwfofBGGxQOgE5ovBrgYEEAhpGwSgYBaNgBAIA9xFBjYo05RUAAAAASUVORK5CYII=","orcid":"","institution":"Universidade de Pernambuco","correspondingAuthor":true,"prefix":"","firstName":"Patricia","middleName":"Takako","lastName":"Endo","suffix":""}],"badges":[],"createdAt":"2025-05-20 23:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6711220/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6711220/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86297048,"identity":"b2a2771b-bc25-4d3c-88c6-fc2012db4ca4","added_by":"auto","created_at":"2025-07-09 05:33:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9776069,"visible":true,"origin":"","legend":"","description":"","filename":"scientificreports2025pretermbia.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6711220/v1_covered_fc6cefda-b143-48c0-a5bc-72a4c41d22d0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine learning for predicting preterm birth: A cross-database analysis on Pernambuco, Brazil cases","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-6711220/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6711220/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Preterm birth remains a major public health challenge globally and is the leading cause of death in children under five. 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Findings indicate that although models generally achieved higher accuracy for term births, sensitivity for preterm cases remained limited, especially when trained with imbalanced data. The undersampling strategy, when coupled with threshold tuning based on ROC curve analysis, resulted in the most favorable trade-off between sensitivity and positive predictive value (PPV). Cross-database evaluation revealed performance degradation when models trained on one dataset were applied to another, highlighting the influence of data heterogeneity and the importance of local context in model development. 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