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Comparative study of machine learning methods for COVID-19 transmission forecasting.

Authors :
Dairi A
Harrou F
Zeroual A
Hittawe MM
Sun Y
Source :
Journal of biomedical informatics [J Biomed Inform] 2021 Jun; Vol. 118, pp. 103791. Date of Electronic Publication: 2021 Apr 26.
Publication Year :
2021

Abstract

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.<br /> (Copyright © 2021 Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1532-0480
Volume :
118
Database :
MEDLINE
Journal :
Journal of biomedical informatics
Publication Type :
Academic Journal
Accession number :
33915272
Full Text :
https://doi.org/10.1016/j.jbi.2021.103791