An Evidential Neural Network Framework with Gaussian Random Fuzzy Numbers for Multi-Disease Risk Prediction and Uncertainty Quantification

An Evidential Neural Network Framework with Gaussian Random Fuzzy Numbers for Multi-Disease Risk Prediction and Uncertainty Quantification | 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 An Evidential Neural Network Framework with Gaussian Random Fuzzy Numbers for Multi-Disease Risk Prediction and Uncertainty Quantification Shamsuddeen Muhammad Abubakar, Abdulmajid Babangida Umar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8189478/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 Nigeria faces an increasing dual burden of communicable and non-communicable diseases. Cardiovascular disorders, diabetes, malaria, and kidney disease together account for more than two-thirds of hospital admissions. Although interest in advanced predictive systems is growing, many current models overlook correlated clinical features and fail to quantify prediction uncertainty. This study presents the Evidential Neural Network with Gaussian Random Fuzzy Numbers (EVNN-GRFN), a hybrid framework designed to model correlation, fuzziness, and uncertainty in diverse medical data. The method converts clinical indicators into Gaussian fuzzy sets, applies penalties to redundant correlations, and integrates probabilistic evidence using Dempster–Shafer theory. Four model variants (M1–M4) were tested on benchmark and Nigerian datasets—UCI Heart, PIMA Diabetes, Malaria Symptoms, and chronic kidney disease using accuracy, F1 score, AUC-ROC, RMSE, and inference delay as evaluation metrics. The EVNN-GRFN-M4 model achieved 97.8% accuracy, an AUC of 0.992, an RMSE of 0.06, and a 42 ms delay, outperforming Random Forest, SVM, and CNN baselines. Statistical testing (Friedman χ²(6) = 16.92, p = 0.009) confirmed the improvements as significant. By providing interpretable, uncertainty-aware outputs, the framework supports clinicians in assessing prediction confidence, consistent with WHO’s trustworthy-health technology guidelines and Nigeria’s Digital Health Strategy. Overall, EVNN-GRFN demonstrates that integrating fuzzy correlation control with evidential reasoning produces reliable, transparent, and efficient diagnostic tools suitable for resource-constrained healthcare environments. Evidential Neural Network Gaussian Fuzzy Logic Multi-Disease Prediction Uncertainty Quantification Nigeria Healthcare Correlated Biomarkers 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-8189478","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557322501,"identity":"f3b8ab0a-360f-4543-952a-5ac0c26b21db","order_by":0,"name":"Shamsuddeen Muhammad 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Quantification, Nigeria Healthcare, Correlated Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-8189478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8189478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNigeria faces an increasing dual burden of communicable and non-communicable diseases. Cardiovascular disorders, diabetes, malaria, and kidney disease together account for more than two-thirds of hospital admissions. Although interest in advanced predictive systems is growing, many current models overlook correlated clinical features and fail to quantify prediction uncertainty. This study presents the Evidential Neural Network with Gaussian Random Fuzzy Numbers (EVNN-GRFN), a hybrid framework designed to model correlation, fuzziness, and uncertainty in diverse medical data. The method converts clinical indicators into Gaussian fuzzy sets, applies penalties to redundant correlations, and integrates probabilistic evidence using Dempster\u0026ndash;Shafer theory. Four model variants (M1\u0026ndash;M4) were tested on benchmark and Nigerian datasets\u0026mdash;UCI Heart, PIMA Diabetes, Malaria Symptoms, and chronic kidney disease using accuracy, F1 score, AUC-ROC, RMSE, and inference delay as evaluation metrics. The EVNN-GRFN-M4 model achieved 97.8% accuracy, an AUC of 0.992, an RMSE of 0.06, and a 42 ms delay, outperforming Random Forest, SVM, and CNN baselines. Statistical testing (Friedman χ\u0026sup2;(6)\u0026thinsp;=\u0026thinsp;16.92, p\u0026thinsp;=\u0026thinsp;0.009) confirmed the improvements as significant. By providing interpretable, uncertainty-aware outputs, the framework supports clinicians in assessing prediction confidence, consistent with WHO\u0026rsquo;s trustworthy-health technology guidelines and Nigeria\u0026rsquo;s Digital Health Strategy. 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