Enhanced conditional GAN for high-quality synthetic tabular data generation in mobile-based cardiovascular healthcare

Alqulaity, M. and Yang, P. orcid.org/0000-0002-8553-7127 (2024) Enhanced conditional GAN for high-quality synthetic tabular data generation in mobile-based cardiovascular healthcare. Sensors, 24 (23). 7673. ISSN 1424-8220

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Item Type: Article
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© 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/).

Keywords: tabular data; generative adversarial networks; synthetic data generation; cardiovascular disease; medical informatics; machine learning in healthcare
Dates:
  • Published: 30 November 2024
  • Published (online): 30 November 2024
  • Accepted: 25 November 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 02 Dec 2024 09:03
Last Modified: 02 Dec 2024 09:03
Published Version: https://www.mdpi.com/1424-8220/24/23/7673
Status: Published
Publisher: MDPI AG
Refereed: Yes
Identification Number: 10.3390/s24237673
Open Archives Initiative ID (OAI ID):

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