RISK CALCULATION OF COVID-19 FOR ASEAN COUNTRIES USING BACKPROPAGATION NEURAL NETWORK AND FUZZY INFERENCE SYSTEM

Abstract

CORONAVIRUS DISEASE (COVID-19) is the infectious disease caused by the coronavirus that was first discovered in Wuhan City, China, and then spread throughout the world. Many researchers have proposed various methods to predict the spread of viruses. Predicting the number of COVID-19 patients is a crucial task in the effort to assist governments and healthcare departments respond rapidly to outbreaks. One type of prediction method is Artificial Neural Network (ANN), which is much more flexible and can handle more complicated and unassuming cases than the regression method. There are many ANN algorithms. Among them, the backpropagation algorithm is used in the proposed system. The backpropagation algorithm is a method for training multilayer feed-forward neural networks. It can be used to solve predictive problems with good results. Firstly, the proposed system implements a prediction model to estimate the number of COVID-19 sufferers in ASEAN Countries using a backpropagation neural network with Gradient Descent and a Backpropagation neural network with Stochastics Gradient Descent Optimizers. Among them, the method that produced the best performance is used to predict the future number of COVID-19 cases. And then these predicted results are used to decide the risk category of a country with Fuzzy Inference System. To evaluate the performance of the prediction methods for the number of COVID-19 sufferers, Root Mean Square Error (RMSE) is used and compared. According to the experimental results, the Backpropagation neural network with Stochastics Gradient Descent method has a better performance than the Backpropagation neural network with Gradient Descent method. The accuracy of the Fuzzy Inference method for the classification of the risk category of each country is calculated many times by using the preexisting actual trend data. As a result, the proposed system can be useful for risk categorization and long-term outbreak prediction in epidemics like COVID-19.