Comparing machine learning with case-control models to identify confirmed dengue cases

Tzong-Shiann Ho, Ting-Chia Weng, Jung- Der Wang, Hsieh-Cheng Han, Hao-Chien Cheng, Chun-Chieh Yang, Chih-Hen Yu, Yen-Jung Liu, Chien Hsiang Hu, Chun-Yu Huang, Ming-Hong Chen, Chwan-Chuen King, Yen-Jen Oyang and Ching-Chuan Liu

PLOS Neglected Tropical Diseases, 2020, vol. 14, issue 11, 1-21

Abstract: In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pntd00:0008843

DOI: 10.1371/journal.pntd.0008843

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