Evaluating undersampling techniques in the prediction of potential congenital syphilis cases using real data from Pernambuco, Brazil

Evaluating undersampling techniques in the prediction of potential congenital syphilis cases using real data from Pernambuco, Brazil | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating undersampling techniques in the prediction of potential congenital syphilis cases using real data from Pernambuco, Brazil Morgana Thalita da Silva Leite, Élisson da Silva Rocha, Igor Vitor Teixeira, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4524217/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 Syphilis can be transmitted congenitally and may cause serious consequences forthe child if not treated. The Programa Mãe Coruja Pernambucana (PMCP) is a brazilian public health program that helps pregnant women and saves data that can be used for prediction of potential congenital syphilis cases, through machine learning models. Only one work was found that predicts congenital syphilis through machine learning. This research uses a different methodology that evaluates undersampling in prediction. Random Undersampling, UnderSampling Based on Clustering (SBC) and NearMiss were used. The data was preprocessed and undersampling applied, generating different balanced datasets to train and test different machine learning models and different metrics for evaluation. Undersampling discarded data evaluation and analysis of distribution of the best attributes were applied to evaluate undersampling in best models. In models results, NearMiss trained models had high metrics, and very low in the discarded data. SBC models had smaller metrics, and in the discarded data went lower. Random Undersampling models had the lowest metrics, however in the discarded data showed similar results. The distribution of best attributes of NearMiss models were not similar to the original, contrary to Random Undersampling and SBC. NearMiss models had best results in the models, through this work evaluation showed that they cannot generalize the PMCP data and have not a representative distribution of the original data. Random Undersampling models had the lowest metrics but showed consistency through the evaluations, and thus are recommended for the congenital syphilis prediction. congenital syphilis machine learning undersampling evaluation 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 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-4524217","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310971884,"identity":"ae5c4b70-cc0d-4fb8-b062-077d16c0ecea","order_by":0,"name":"Morgana Thalita da Silva Leite","email":"","orcid":"","institution":"Universidade de Pernambuco","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Morgana","middleName":"Thalita da Silva","lastName":"Leite","suffix":""},{"id":310971885,"identity":"95520291-0d7e-40e5-8d04-8ac04cf8e76b","order_by":1,"name":"Élisson da Silva Rocha","email":"","orcid":"","institution":"Universidade de Pernambuco","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Élisson","middleName":"da Silva","lastName":"Rocha","suffix":""},{"id":310971886,"identity":"68cd77ff-ca02-4aa2-a6fd-8a4042eeac50","order_by":2,"name":"Igor Vitor Teixeira","email":"","orcid":"","institution":"Universidade de Pernambuco","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Igor","middleName":"Vitor","lastName":"Teixeira","suffix":""},{"id":310971887,"identity":"cdeee9dd-b0ef-4be7-9276-8b7d0a1a5e54","order_by":3,"name":"Flávio Leandro de Morais Melo","email":"","orcid":"","institution":"Universidade de Pernambuco","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Flávio","middleName":"Leandro de Morais","lastName":"Melo","suffix":""},{"id":310971888,"identity":"a36e242b-7754-4d77-925f-83e851c93baa","order_by":4,"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,"submittingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"Takako","lastName":"Endo","suffix":""}],"badges":[],"createdAt":"2024-06-03 23:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4524217/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4524217/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63451328,"identity":"030ddfc8-fab7-4b37-92bc-a5d7286735fa","added_by":"auto","created_at":"2024-08-28 09:26:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170866,"visible":true,"origin":"","legend":"","description":"","filename":"congenitalsyphilismorgana.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4524217/v1_covered_404bd7e2-5e47-4ae0-9d67-cdfbdbf3b6d7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating undersampling techniques in the prediction of potential congenital syphilis cases using real data from Pernambuco, Brazil","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":"congenital syphilis, machine learning, undersampling, evaluation","lastPublishedDoi":"10.21203/rs.3.rs-4524217/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4524217/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Syphilis can be transmitted congenitally and may cause serious consequences forthe child if not treated. The Programa Mãe Coruja Pernambucana (PMCP) is a brazilian public health program that helps pregnant women and saves data that can be used for prediction of potential congenital syphilis cases, through machine learning models. Only one work was found that predicts congenital syphilis through machine learning. This research uses a different methodology that evaluates undersampling in prediction. Random Undersampling, UnderSampling Based on Clustering (SBC) and NearMiss were used. The data was preprocessed and undersampling applied, generating different balanced datasets to train and test different machine learning models and different metrics for evaluation. Undersampling discarded data evaluation and analysis of distribution of the best attributes were applied to evaluate undersampling in best models. In models results, NearMiss trained models had high metrics, and very low in the discarded data. SBC models had smaller metrics, and in the discarded data went lower. Random Undersampling models had the lowest metrics, however in the discarded data showed similar results. The distribution of best attributes of NearMiss models were not similar to the original, contrary to Random Undersampling and SBC. NearMiss models had best results in the models, through this work evaluation showed that they cannot generalize the PMCP data and have not a representative distribution of the original data. Random Undersampling models had the lowest metrics but showed consistency through the evaluations, and thus are recommended for the congenital syphilis prediction.","manuscriptTitle":"Evaluating undersampling techniques in the prediction of potential congenital syphilis cases using real data from Pernambuco, Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 20:13:59","doi":"10.21203/rs.3.rs-4524217/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"34c6d4fd-a1b2-4583-bff2-58a94a60518e","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-11T07:59:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-19 20:13:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4524217","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4524217","identity":"rs-4524217","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}