Machine learning approaches classify clinical malaria outcomes based on haematological parameters

Machine learning approaches classify clinical malaria outcomes based on haematological parameters

Abstract Background Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge....

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Bibliographic Details
Main Authors: Collins M. Morang’a, Lucas Amenga–Etego, Saikou Y. Bah, Vincent Appiah, Dominic S. Y. Amuzu, Nicholas Amoako, James Abugri, Abraham R. Oduro, Aubrey J. Cunnington, Gordon A. Awandare, Thomas D. Otto
Format: Article
Language:English
Published: BMC 2020-11-01
Series:BMC Medicine
Subjects:
Machine learning
Uncomplicated Malaria
Severe Malaria
Haematological parameters
Classification
Online Access:https://doi.org/10.1186/s12916-020-01823-3

Internet

https://doi.org/10.1186/s12916-020-01823-3

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