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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 | 2024-07-28T14:10:29Z | - |
dc.date.available | 2024-07-28T14:10:29Z | - |
dc.date.issued | 2024-04-15 | - |
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 | 379 | - |
dc.identifier.citation | Al Sadi, K. and Balachandran, W. (2024) 'Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach', Bioengineering, 11 (4), 379, pp. 1 - 13. doi: 10.3390/bioengineering11040379. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29446 | - |
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 | This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of ‘Artificial Intelligence in Healthcare’. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman’s population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model’s performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model’s capability in diabetes detection. The implementation of this LSTM model in Oman’s healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges relating to globally diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman. | en_US |
dc.description.sponsorship | This research received no external funding. The article processing charges were covered by Brunel University London. | en_US |
dc.format.extent | 1 - 13 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2024 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 | artificial intelligence | en_US |
dc.subject | LSTM | en_US |
dc.subject | diabetes prediction | en_US |
dc.subject | predictive healthcare | en_US |
dc.subject | Oman | en_US |
dc.subject | early detection | en_US |
dc.subject | public health | en_US |
dc.title | Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-04-12 | - |
dc.identifier.doi | https://doi.org/10.3390/bioengineering11040379 | - |
dc.relation.isPartOf | Bioengineering | - |
pubs.issue | 4 | - |
pubs.publication-status | Published | - |
pubs.volume | 11 | - |
dc.identifier.eissn | 2306-5354 | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
dc.rights.holder | The authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2024 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/). | 1.99 MB | Adobe PDF | View/Open |
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