Conference Paper/Proceeding/Abstract 393 views 63 downloads
Maximizing Sample Utilization in CKD Classification: Fusion and Alignment of Locally Trained Models with a Global Model
,
James Chess,
Xianghua Xie
2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Pages: 1 - 7
Swansea University Authors:
Ali Guran, Avishek Siris, Gary Tam , Xianghua Xie
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DOI (Published version): 10.1109/embc58623.2025.11253104
Abstract
Maximizing Sample Utilization in CKD Classification: Fusion and Alignment of Locally Trained Models with a Global Model
| Published in: | 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
|---|---|
| ISBN: | 979-8-3315-8619-5 979-8-3315-8618-8 |
| ISSN: | 2375-7477 2694-0604 |
| Published: |
IEEE
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa69419 |
| Keywords: |
Accuracy, Limiting, Soft sensors, Medical services, Feature extraction, Chronic kidney disease, Data models, Imputation, Faces, Blood |
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| College: |
Faculty of Science and Engineering |
| Funders: |
Ali Guran received funding from the Turkish Ministry of National Education (Republic of Türkiye) through the Postgraduate Study Abroad Program. Gary Tam was sup-ported by the International Mobility Award [62] and the Collaboration and Knowledge Exchange Support [92S], both provided by the CHERISH-DE Centre (EP/M022722/1). This work is partly supported by the EPSRC National Edge AI Hub (EP/Y007697/1). This project also benefited in part from a fee waiver fund provided by SAIL. |
| Start Page: |
1 |
| End Page: |
7 |