An Optimized Model Based on Deep Learning and Gated Recurrent Unit for COVID-19 Death Prediction
Zahraa Tarek,
Mahmoud Y. Shams,
S. K. Towfek,
Hend K. Alkahtani,
Abdelhameed Ibrahim,
Abdelaziz A. Abdelhamid,
Marwa M. Eid,
Nima Khodadadi,
Laith Abualigah,
Doaa Sami Khafaga,
Ahmed M. Elshewey
Affiliations
Zahraa Tarek
Computer Science Department, Faculty of Computers and Information, Mansoura University, Mansoura 35561, Egypt
Mahmoud Y. Shams
Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
S. K. Towfek
Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA
Hend K. Alkahtani
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdelhameed Ibrahim
Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Abdelaziz A. Abdelhamid
Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Marwa M. Eid
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Nima Khodadadi
Department of Civil and Architectural Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, FL 33146, USA
Laith Abualigah
Computer Science Department, Al al-Bayt University, Mafraq 25113, Jordan
Doaa Sami Khafaga
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Ahmed M. Elshewey
Computer Science Department, Faculty of Computers and Information, Suez University, Suez 43512, Egypt
The COVID-19 epidemic poses a worldwide threat that transcends provincial, philosophical, spiritual, radical, social, and educational borders. By using a connected network, a healthcare system with the Internet of Things (IoT) functionality can effectively monitor COVID-19 cases. IoT helps a COVID-19 patient recognize symptoms and receive better therapy more quickly. A critical component in measuring, evaluating, and diagnosing the risk of infection is artificial intelligence (AI). It can be used to anticipate cases and forecast the alternate incidences number, retrieved instances, and injuries. In the context of COVID-19, IoT technologies are employed in specific patient monitoring and diagnosing processes to reduce COVID-19 exposure to others. This work uses an Indian dataset to create an enhanced convolutional neural network with a gated recurrent unit (CNN-GRU) model for COVID-19 death prediction via IoT. The data were also subjected to data normalization and data imputation. The 4692 cases and eight characteristics in the dataset were utilized in this research. The performance of the CNN-GRU model for COVID-19 death prediction was assessed using five evaluation metrics, including median absolute error (MedAE), mean absolute error (MAE), root mean squared error (RMSE), mean square error (MSE), and coefficient of determination (R2). ANOVA and Wilcoxon signed-rank tests were used to determine the statistical significance of the presented model. The experimental findings showed that the CNN-GRU model outperformed other models regarding COVID-19 death prediction.
