tetano
Editor, Senior Moderator
J Biomed Inform
. 2021 Apr 26;103791.
doi: 10.1016/j.jbi.2021.103791. Online ahead of print.
Comparative study of machine learning methods for COVID-19 transmission forecasting
Abdelkader Dairi[SUP] 1 [/SUP], Fouzi Harrou[SUP] 2 [/SUP], Abdelhafid Zeroual[SUP] 3 [/SUP], Mohamad Mazen Hittawe[SUP] 4 [/SUP], Ying Sun[SUP] 4 [/SUP]
Affiliations
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.
Keywords: COVID-19; GAN-GRU.; Hybrid deep learning; LSTM-CNN; short-term forecasting.
. 2021 Apr 26;103791.
doi: 10.1016/j.jbi.2021.103791. Online ahead of print.
Comparative study of machine learning methods for COVID-19 transmission forecasting
Abdelkader Dairi[SUP] 1 [/SUP], Fouzi Harrou[SUP] 2 [/SUP], Abdelhafid Zeroual[SUP] 3 [/SUP], Mohamad Mazen Hittawe[SUP] 4 [/SUP], Ying Sun[SUP] 4 [/SUP]
Affiliations
- PMID: 33915272
- DOI: 10.1016/j.jbi.2021.103791
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.
Keywords: COVID-19; GAN-GRU.; Hybrid deep learning; LSTM-CNN; short-term forecasting.