A comparative study of imputation techniques for missing values in healthcare diagnostic datasets

Joel, L.O., Doorsamy, W. orcid.org/0000-0001-9043-9882 and Paul, B.S. (2025) A comparative study of imputation techniques for missing values in healthcare diagnostic datasets. International Journal of Data Science and Analytics. ISSN 2364-415X

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Item Type: Article
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© The Author(s) 2025. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Missing data imputation; Healthcare datasets; Machine learning; Imputation techniques; MissForest
Dates:
  • Accepted: 20 May 2025
  • Published (online): 11 June 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 07 Jul 2025 14:50
Last Modified: 07 Jul 2025 14:50
Status: Published
Publisher: Springer Nature
Identification Number: 10.1007/s41060-025-00825-9
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 3: Good Health and Well-Being
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