Imperial College London
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Dr Aubrey Cunnington

Faculty of Medicine, Department of Infectious Disease

Professor of Paediatric Infectious Disease
 
 
 
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Contact

 

+44 (0)20 7594 3695a.cunnington

 
 
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Location

 

244Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Moranga:2020:10.1101/2020.09.23.20200220,
author = {Moranga, CM and AmengaEtego, L and Bah, SY and Appiah, V and Amuzu, DS and Amoako, N and Abugri, J and Oduro, AR and Cunnington, AJ and Awandare, GA and Otto, TD},
doi = {10.1101/2020.09.23.20200220},
title = {Machine learning approaches classify clinical malaria outcomes based on haematological parameters},
url = {http://dx.doi.org/10.1101/2020.09.23.20200220},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - <jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>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 (RDT) is threatened by<jats:italic>Pfhrp2/3</jats:italic>deletions and decreased sensitivity at low parasitemia. Analysis of haematological indices can be used to support identification of possible malaria cases for further diagnosis, especially in travelers 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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We obtained haematological data from 2,207 participants collected in Ghana; nMI (n=978), UM (n=526), and SM (n=703). Six different machine learning 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-agonistic explanations (LIME) were used to explain the binary classifiers.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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 =
AU  - Moranga,CM
AU  - AmengaEtego,L
AU  - Bah,SY
AU  - Appiah,V
AU  - Amuzu,DS
AU  - Amoako,N
AU  - Abugri,J
AU  - Oduro,AR
AU  - Cunnington,AJ
AU  - Awandare,GA
AU  - Otto,TD
DO  - 10.1101/2020.09.23.20200220
PY  - 2020///
TI  - Machine learning approaches classify clinical malaria outcomes based on haematological parameters
UR  - http://dx.doi.org/10.1101/2020.09.23.20200220
ER  -
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