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Modeling Plasmodium falciparum Diagnostic Test Sensitivity Using Machine Learning With Histidine-Rich Protein 2 Variants
Journal article   Open access   Peer reviewed

Modeling Plasmodium falciparum Diagnostic Test Sensitivity Using Machine Learning With Histidine-Rich Protein 2 Variants

Colby T. Ford, Gezahegn Solomon Alemayehu, Kayla Blackburn, Karen Lopez, Cheikh Cambel Dieng, Lemu Golassa, Eugenia Lo and Daniel Janies
Frontiers in tropical diseases, v 2, 707313
04 Oct 2021
DOI: https://doi.org/10.3389/fitd.2021.707313
url
https://doi.org/10.3389/fitd.2021.707313View
Published, Version of Record (VoR) Open

Abstract

model explainability Plasmodium falciparum rapid diagnostic test Malaria Machine Learning
Malaria, predominantly caused by Plasmodium falciparum, poses one of largest and most durable health threats in the world. Previously, simplistic regression-based models have been created to characterize malaria rapid diagnostic test performance, though these models often only include a couple genetic factors. Specifically, the Baker et al., 2005 model uses two types of particular repeats in histidine-rich protein 2 (PfHRP2) to describe a P. falciparum infection, though the efficacy of this model has waned over recent years due to genetic mutations in the parasite. In this work, we use a dataset of 100 P. falciparum PfHRP2 genetic sequences collected in Ethiopia and derived a larger set of motif repeat matches for use in generating a series of diagnostic machine learning models. Here we show that the usage of additional and different motif repeats in more sophisticated machine learning methods proves effective in characterizing PfHRP2 diversity. Furthermore, we use machine learning model explainability methods to highlight which of the repeat types are most important with regards to rapid diagnostic test sensitivity, thereby showcasing a novel methodology for identifying potential targets for future versions of rapid diagnostic tests.

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