Bayesian and Machine Learning Approaches to the Classification Problem

Bayesian and Machine Learning Approaches to the Classification Problem | 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 Bayesian and Machine Learning Approaches to the Classification Problem Romuald Daniel BOY-NGBOGBELE This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9621535/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 Background: Classification problems are fundamental in statistical learning and arise in numerous fields such as healthcare, finance, and environmental sciences. Traditional statistical models provide interpretable parameter estimates but may struggle to capture complex nonlinear relationships, whereas machine learning methods often achieve strong predictive performance at the cost of interpretability. These limitations motivate the development of hybrid approaches that integrate probabilistic modeling with modern machine learning techniques. Methods: This study proposes a Bayesian–Machine Learning Ensemble (BMLE) framework that combines Bayesian logistic regression with a machine learning component to improve classification performance while maintaining interpretability. A simulation study was conducted to evaluate the proposed model and compare its performance with a conventional logistic regression model. Model performance was assessed using several classification metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC). In addition, parameter estimation was evaluated using bias, root mean squared error (RMSE), and coverage probability. Results: The simulation results demonstrate that the proposed BMLE model consistently outperforms the conventional logistic regression model across all predictive performance metrics. The corrected model achieved higher accuracy, precision, recall, and F1-score, as well as a higher AUC value, indicating improved discriminative ability. Furthermore, the Bayesian component provided stable parameter estimates and meaningful uncertainty quantification through credible intervals. Conclusion: The proposed Bayesian–Machine Learning Ensemble framework offers a flexible and robust approach for classification problems by integrating probabilistic inference with machine learning techniques. The results suggest that the hybrid modeling strategy improves predictive accuracy while preserving interpretability, making it a promising methodology for complex classification tasks. Biostatistics Imbalanced Classification Bayesian Inference Machine Learning Hybrid Ensemble Methods Probabilistic Modeling Full Text Additional Declarations The authors declare no competing interests. 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-9621535","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634980858,"identity":"41684e0a-38c0-4918-ad11-a6e591df06e0","order_by":0,"name":"Romuald Daniel BOY-NGBOGBELE","email":"data:image/png;base64,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","orcid":"","institution":"Pan African University, Nairobi","correspondingAuthor":true,"prefix":"","firstName":"Romuald","middleName":"Daniel","lastName":"BOY-NGBOGBELE","suffix":""}],"badges":[],"createdAt":"2026-05-05 17:30:58","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":true,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9621535/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9621535/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108667960,"identity":"d02294cf-323e-4d64-a9db-e09215e834d1","added_by":"auto","created_at":"2026-05-07 06:57:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":389059,"visible":true,"origin":"","legend":"","description":"","filename":"Article7RomualdDaniel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9621535/v1_covered_7f0529a5-9de8-4e30-b469-42e475f67eab.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBayesian and Machine Learning Approaches to the Classification Problem\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Adwa-Pan African University","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":"Imbalanced Classification, Bayesian Inference, Machine Learning, Hybrid Ensemble Methods, Probabilistic Modeling","lastPublishedDoi":"10.21203/rs.3.rs-9621535/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9621535/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u0026nbsp;Background:\u003c/p\u003e\n\u003cp\u003eClassification problems are fundamental in statistical learning and arise in numerous fields such as healthcare, finance, and environmental sciences. Traditional statistical models provide interpretable parameter estimates but may struggle to capture complex nonlinear relationships, whereas machine learning methods often achieve strong predictive performance at the cost of interpretability. These limitations motivate the development of hybrid approaches that integrate probabilistic modeling with modern machine learning techniques.\u003c/p\u003e\n\u003cp\u003eMethods:\u003c/p\u003e\n\u003cp\u003eThis study proposes a Bayesian–Machine Learning Ensemble (BMLE) framework that combines Bayesian logistic regression with a machine learning component to improve classification performance while maintaining interpretability. A simulation study was conducted to evaluate the proposed model and compare its performance with a conventional logistic regression model. Model performance was assessed using several classification metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC). In addition, parameter estimation was evaluated using bias, root mean squared error (RMSE), and coverage probability.\u003c/p\u003e\n\u003cp\u003eResults:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe simulation results demonstrate that the proposed BMLE model consistently outperforms the conventional logistic regression model across all predictive performance metrics. The corrected model achieved higher accuracy, precision, recall, and F1-score, as well as a higher AUC value, indicating improved discriminative ability. 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