Open Access

ARTICLE

Artificial Neural Networks for Prediction of COVID-19 in Saudi Arabia

Nawaf N. Hamadneh¹, Waqar A. Khan², Waqar Ashraf³, Samer H. Atawneh⁴, Ilyas Khan^5,*, Bandar N. Hamadneh⁶

1 Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
2 Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia
3 Department of Mathematics and Natural Sciences, College of Sciences & Human Studies, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia
4 College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
5 Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, 72915, Vietnam
6 King Abdullah University Hospital, University of Science and Technology, Irbid, 22110, Jordan

* Corresponding Author: Ilyas Khan. Email:

(This article belongs to the Special Issue: Machine Learning and Computational Methods for COVID-19 Disease Detection and Prediction)

Computers, Materials & Continua 2021, 66(3), 2787-2796. https://doi.org/10.32604/cmc.2021.013228

Received 30 July 2020; Accepted 13 September 2020; Issue published 28 December 2020

Abstract

In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation—Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN–PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.

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Citations