Machine learning approaches classify clinical malaria outcomes based on haematological parameters

Morang’a, C.M., Amenga–Etego, L., Bah, S.Y. orcid.org/0000-0002-0309-6509 et al. (8 more authors) (2020) Machine learning approaches classify clinical malaria outcomes based on haematological parameters. BMC Medicine, 18 (1). 375.

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © The Author(s). 2020 .This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Keywords: Machine learning; Uncomplicated Malaria; Severe Malaria; Haematological parameters; Classification
Dates:
  • Accepted: 22 October 2020
  • Published (online): 30 November 2020
  • Published: December 2020
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Biological Sciences (Sheffield) > Department of Molecular Biology and Biotechnology (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 28 Jan 2021 11:38
Last Modified: 28 Jan 2021 11:38
Status: Published
Publisher: Springer Science and Business Media LLC
Refereed: Yes
Identification Number: https://doi.org/10.1186/s12916-020-01823-3
Related URLs:

Share / Export

Statistics