Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning | 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 Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning Chukwudi Obinna Nwokoro, Boluwaji Ade Akinnuwesi, Sourabh Shastri, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3919473/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract The significance of maternal health cannot be overemphasized, and the ability to predict maternal outcomes accurately is critical to ensuring the well-being of both mothers and infants. This study presents ConvXGB, a novel predictive model that utilizes a combination of XGBoost, a potent gradient boosting algorithm, and deep learning to extract intricate features. The objective is to enhance precision and robustness of maternal outcome predictions. The study sourced diverse maternal health data from the southern region of Nigeria and implemented Synthetic Minority Over-sampling Technique (SMOTE) to address any dataset imbalances. Results obtain demonstrate a significant improvement in model performance, with an accuracy rate of 97.96% across various maternal outcome classes. The recommendations from this study highlight the potential of ConvXGB in advancing maternal health predictive analytics, supporting informed clinical decision-making, and improving resource allocation. Further studies are warranted to explore the broader applicability of ConvXGB in different healthcare domains through outcome analyses and methodological advancements. Computational Biology Maternal health Outcome prediction XGBoost Deep learning Convolutional Neural Network Predictive modelling Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-3919473","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270622715,"identity":"a5ddff7a-bc51-476b-9059-0675ff6c61e7","order_by":0,"name":"Chukwudi Obinna Nwokoro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYHACZjBpwMAGJNkk5PhBvIQCErQYSzaAtBgQr4UhccMBCBcnMJdIPmzwcY+NvLlEWpo0T5lF4ubzqxM/PDBgkOcXO4BVi+WMtOTEGc/SDHfOSDsmzXNOwnjbjbebJYAOM5w5OwGrFoPbOcaHeQ4cTjC4kd52m7dNQnbbjbMbQFoSDG4TqYVx84yzm38Q0pIM0ZJ2DKRFcQN/7zb8ttx/lmw440Ca4YYzz9J/zgH6ReIG7zaLBAMJ3H45c/iwxIcDNvIGx9OMDd6U1cnx95/dfPNHhY08vzR2LViABFilBLHKQYD/ACmqR8EoGAWjYAQAAHo2Y0kJH9C3AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-0886-0289","institution":"Department of Computer Science, Faculty of Computing, University of Uyo, Nigeria.","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chukwudi","middleName":"Obinna","lastName":"Nwokoro","suffix":""},{"id":270622716,"identity":"82de6f76-1750-48bd-b685-ef1037d85dbb","order_by":1,"name":"Boluwaji Ade Akinnuwesi","email":"","orcid":"","institution":"Department of Computer Science, Faculty of Science and Enginerring, Univeristy of Eswatini, Kwaluseni, Eswatini.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Boluwaji","middleName":"Ade","lastName":"Akinnuwesi","suffix":""},{"id":270622717,"identity":"1a871fe6-1095-4d18-bffb-530824d59a5d","order_by":2,"name":"Sourabh Shastri","email":"","orcid":"","institution":"Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, Kathua 184104, India","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sourabh","middleName":"","lastName":"Shastri","suffix":""},{"id":270622718,"identity":"2b07976b-98f1-42e0-8afb-8198c0372912","order_by":3,"name":"Faith-Michael Uzoka","email":"","orcid":"","institution":"Mathematics and Computing Department, Mount Royal University. 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