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DC Field | Value | Language |
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dc.contributor.author | Al Sadi, K | - |
dc.contributor.author | Balachandran, W | - |
dc.date.accessioned | 2024-02-19T09:39:15Z | - |
dc.date.available | 2024-02-19T09:39:15Z | - |
dc.date.issued | 2023-12-14 | - |
dc.identifier | ORCiD: Khoula Al Sadi https://orcid.org/0000-0001-6077-4110 | - |
dc.identifier | ORCiD: Wamadeva Balachandran https://orcid.org/0000-0002-4806-2257 | - |
dc.identifier | 1420 | - |
dc.identifier.citation | Al Sadi, K. and Balachandran, W. (2023) 'Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman', Bioengineering, 10 (12), 1420, pp. 1 - 22. doi: 10.3390/bioengineering10121420. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/28338 | - |
dc.description | Data Availability Statement: The uniquely constructed Oman Diabetes Type II Screening Dataset, which substantiates the findings of this study, can be made available upon reasonable request by contacting the corresponding author. | en_US |
dc.description.abstract | The surge of diabetes poses a significant global health challenge, particularly in Oman and the Middle East. Early detection of diabetes is crucial for proactive intervention and improved patient outcomes. This research leverages the power of machine learning, specifically Convolutional Neural Networks (CNNs), to develop an innovative 4D CNN model dedicated to early diabetes prediction. A region-specific dataset from Oman is utilized to enhance health outcomes for individuals at risk of developing diabetes. The proposed model showcases remarkable accuracy, achieving an average accuracy of 98.49% to 99.17% across various epochs. Additionally, it demonstrates excellent F1 scores, recall, and sensitivity, highlighting its ability to identify true positive cases. The findings contribute to the ongoing effort to combat diabetes and pave the way for future research in using deep learning for early disease detection and proactive healthcare. | en_US |
dc.description.sponsorship | This research received no external funding. | en_US |
dc.format.extent | 1 - 22 | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | deep learning | en_US |
dc.subject | convolutional neural networks (CNNs) | en_US |
dc.subject | k-nearest neighbours (KNN) | en_US |
dc.subject | diabetes type II | en_US |
dc.title | Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.3390/bioengineering10121420 | - |
dc.relation.isPartOf | Bioengineering | - |
pubs.issue | 12 | - |
pubs.publication-status | Published | - |
pubs.volume | 10 | - |
dc.identifier.eissn | 2306-5354 | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 7.16 MB | Adobe PDF | View/Open |
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