Comparative study of machine learning methods for COVID-19 transmission forecasting.
Comparative study of machine learning methods for COVID-19 transmission forecasting.
复制标题
COVID-19传播预测的机器学习方法比较研究。
DOI：
10.1016/j.jbi.2021.103791
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发表时间：
2021-06
影响因子：
4.5
通讯作者：
Sun Y
中科院分区：
文献类型：
--
作者：
Dairi A;Harrou F;Zeroual A;Hittawe MM;Sun Y
在最近的大流行中，科学家和临床医生致力于寻求新技术来阻止或减缓COVID-19大流行。机器学习作为人工智能的一个重要方面，在过去的流行病中的好处为应对新型冠状病毒疫情提供了一条新的路线。对COVID-19传播的准确短期预测在改善医院过度拥挤问题的管理方面发挥着至关重要的作用，并能够适当优化可用资源（即，本文介绍了机器学习方法用于COVID-19传播预测的比较研究。我们研究了深度学习方法的性能，包括混合卷积神经网络-长短期记忆（LSTM-CNN），混合门控递归单元卷积神经网络（GAN-GRU），GAN，CNN，LSTM和限制玻尔兹曼机（RBM），以及基线机器学习方法，即逻辑回归（LR）和支持向量回归（SVR）。混合模型的使用（即，LSTM-CNN和GAN-GRU）预计最终将提高COVID-19未来趋势的预测准确性。研究人员使用来自七个受影响国家（巴西、法国、印度、墨西哥、俄罗斯、沙特阿拉伯和美国）的确诊和恢复的COVID-19病例时间序列数据测试了所研究的深度学习和机器学习模型的性能。结果表明，混合深度学习模型可以有效地预测COVID-19病例。此外，结果证实了深度学习模型的上级性能优于两个考虑的基线机器学习模型。此外，结果表明，LSTM-CNN的性能得到了改善，平均绝对百分比误差为3.718%。
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.
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DOI：
10.1007/s10489-020-01904-z
发表时间：
2021
期刊：
Applied intelligence (Dordrecht, Netherlands)
影响因子：
--
作者：
Goel T;Murugan R;Mirjalili S;Chakrabartty DK
通讯作者：
Chakrabartty DK
影响因子：
7.8
作者：
Abbasi, Zohreh;Zamani, Iman;Ibeas, Asier
通讯作者：
Ibeas, Asier
影响因子：
4.3
作者：
Dairi, Abdelkader;Harrou, Fouzi;Senouci, Mohamed
通讯作者：
Senouci, Mohamed
DOI：
10.1016/j.isprsjprs.2019.08.015
发表时间：
2019-10-01
影响因子：
12.7
作者：
Barbat, Mauro M.;Wesche, Christine;Mata, Mauricio M.
通讯作者：
Mata, Mauricio M.
影响因子：
--
作者：
Cao, LJ;Tay, FEH
通讯作者：
Tay, FEH