In this section
@article{Morang'a:2020:10.1186/s12916-020-01823-3,
author = {Morang'a, CM and Amenga-Etego, L and Bah, SY and Appiah, V and Amuzu, DSY and Amoako, N and Abugri, J and Oduro, AR and Cunnington, AJ and Awandare, GA and Otto, TD},
doi = {10.1186/s12916-020-01823-3},
journal = {BMC Medicine},
title = {Machine learning approaches classify clinical malaria outcomes based on haematological parameters},
url = {http://dx.doi.org/10.1186/s12916-020-01823-3},
volume = {18},
year = {2020}
}
TY - JOUR
AB - 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. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters. METHODS: We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers. RESULTS: The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used
AU - Morang'a,CM
AU - Amenga-Etego,L
AU - Bah,SY
AU - Appiah,V
AU - Amuzu,DSY
AU - Amoako,N
AU - Abugri,J
AU - Oduro,AR
AU - Cunnington,AJ
AU - Awandare,GA
AU - Otto,TD
DO - 10.1186/s12916-020-01823-3
PY - 2020///
SN - 1741-7015
TI - Machine learning approaches classify clinical malaria outcomes based on haematological parameters
T2 - BMC Medicine
UR - http://dx.doi.org/10.1186/s12916-020-01823-3
UR - https://www.ncbi.nlm.nih.gov/pubmed/33250058
UR - http://hdl.handle.net/10044/1/85002
VL - 18
ER -
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