Author: Bahri, S.; Kdayem, M.; Zoghlami, N.
Title: Long Short-Term Memory based RNN for COVID-19 disease prediction Cord-id: omcsas40 Document date: 2021_1_1
ID: omcsas40
Snippet: Currently, the global health system is suffering from an overwhelming issue affecting a large number of individuals all around the world. The novel coronavirus, called COVID-19, has continued to claim more than one million lives. In such cases, it is of vital importance to develop alternatives addressing this health issue and saving more lives. Artificial Intelligence were among the efficient tools that can address this global threat. In this study, we propose to test a recurrent neural network
Document: Currently, the global health system is suffering from an overwhelming issue affecting a large number of individuals all around the world. The novel coronavirus, called COVID-19, has continued to claim more than one million lives. In such cases, it is of vital importance to develop alternatives addressing this health issue and saving more lives. Artificial Intelligence were among the efficient tools that can address this global threat. In this study, we propose to test a recurrent neural network named Long Short-Term Memory (LSTM-RNN) for estimating the number of future fatality cases in USA, India and Italy. Our experimentations proved the effectiveness of LSTM-RNN in predicting the number of deceased cases with minimum of loss ranging from 1.37% to 2.7%. © 2021 IEEE.
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