Publication
Comparison of neural network architectures for diabetes prediction
| datacite.subject.fos | Ciências Médicas::Ciências da Saúde | |
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Guerreiro, Nathan Antonio | |
| dc.contributor.author | Nijo, Rui | |
| dc.contributor.author | Teixeira, João Paulo | |
| dc.date.accessioned | 2025-06-26T14:31:30Z | |
| dc.date.available | 2025-06-26T14:31:30Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Diabetes represents a significant global health challenge, with millions of individuals affected and substantial impacts on healthcare systems. In this study, we compare two neural network architectures for diabetes prediction: the Feedforward Neural Network (FFNN) and the Cascade-Forward Backpropagation Neural Network (CFBPNN). Utilizing the Diabetes Prediction Dataset, comprising 100,000 samples, and after a balanced result, 17,000 samples were obtained. The networks are trained using the Levenberg-Marquardt and Resilient Backpropagation algorithms, and performance metrics, including precision, sensitivity, specificity, accuracy, F1-score, and computational time, are evaluated. Results indicate that the FFNN architecture paired with the Levenberg-Marquardt algorithm demonstrates superior diagnostic prediction accuracy with 91,10%. However, this comes at the cost of longer computational time compared to the CFBPNN. | eng |
| dc.description.sponsorship | The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI, UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). | |
| dc.identifier.citation | Guerreiro, Nathan Antonio; Nijo, Rui; Teixeira, João Paulo.(2025). Comparison of neural network architectures for diabetes prediction. Procedia Computer Science. ISSN 1877-0509. 256, p. 942-948 | |
| dc.identifier.doi | 10.1016/j.procs.2025.02.198 | |
| dc.identifier.issn | 1877-0509 | |
| dc.identifier.uri | http://hdl.handle.net/10198/34624 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier | |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Research Centre in Digitalization and Intelligent Robotics | |
| dc.relation | Associate Laboratory for Sustainability and Tecnology in Mountain Regions | |
| dc.relation.ispartof | Procedia Computer Science | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Neural Network Architectures | |
| dc.subject | Diabetes Prediction | |
| dc.subject | Machine Learning | |
| dc.title | Comparison of neural network architectures for diabetes prediction | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Research Centre in Digitalization and Intelligent Robotics | |
| oaire.awardTitle | Associate Laboratory for Sustainability and Tecnology in Mountain Regions | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT | |
| oaire.citation.endPage | 948 | |
| oaire.citation.startPage | 942 | |
| oaire.citation.title | Procedia Computer Science | |
| oaire.citation.volume | 256 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Teixeira | |
| person.givenName | João Paulo | |
| person.identifier | 663194 | |
| person.identifier.ciencia-id | 4F15-B322-59B4 | |
| person.identifier.orcid | 0000-0002-6679-5702 | |
| person.identifier.rid | N-6576-2013 | |
| person.identifier.scopus-author-id | 57069567500 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
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