Performance evaluation of multi-layer perceptron (MLP) and radial basis function (RBF): COVID-19 spread and death contributing factors

The Artificial Neural Network (ANN) is an Artificial Intelligence technique which has the ability to learn from experiences, enhancing its performance by adapting to the environmental changes The key benefits of neural networks are the prospect of processing vast quantities of data effectively, and their ability to generalize outcomes Considering the great potential of this technique, this paper aims to establish a performance evaluation of Multilayer Perceptron (MLP) and a Radial Basis Function (RBF) networks in investigating the contributing factors for COVID-19spread and death The RBF and MLP networks are typically used in the same form of applications, however, their internal calculation structures are different A comparison was made by using a dataset of COVID-19 cases in 41 Asia countries during April 2020 There are nine contributing factors which acted as the covariates to the network such as Cases, Deaths, High Temperature, Low Temperature, Population, Percentage of Cases over Population, and Percentage of Death over Population, Average Temperature, and Total Cases The results obtained from the testing sets indicated that the two neural structures were able to investigate the COVID-19 spread and death contributing factors Nevertheless, the RBFnetwork indicated a slightly better performance than the MLP © 2020, World Academy of Research in Science and Engineering All rights reserved