Comparative Evaluation of the Multilayer Perceptron Approach with Conventional ARIMA in Modeling and Prediction of COVID-19 Daily Death Cases

Abstract

COVID-19 continues to pose a dangerous global health threat, as cases grow rapidly and deaths increase day by day. This increasing phenomenon does not only affect economic policy but also international policy around the world. In this paper, Pakistan daily death cases of COVID-19, from February 25, 2020, to March 23, 2022, have been modeled using the long-established autoregressive-integrated moving average (ARIMA) model and the machine learning multilayer perceptron (MLP) model. The most befitting model is selected based on the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Values of the key performance indicator (KPI) showed that the MLP model outperformed the ARIMA model. The MLP model with 20 hidden layers, which emerged as the overall most apt model, was used to predict future daily COVID-19 deaths in Pakistan to enable policymakers and health professionals to put in place systematic measures to reduce death cases. We encourage the Government of Pakistan to intensify its vaccination campaign and encourage everyone to get vaccinated.

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“…(2) Multilayer Perceptron (MLP) Model. Te structure of the multilayer perceptron (MLP) model [47][48][49][50] is composed of the input layer, hidden layer, and output layer with limited nonlinear functions that are diferentiable. In the MLP, information in the form of a nonlinear diferentiable function is processed by artifcial neurons from the input layer through the hidden layer to the output layer, yielding a response in the form of a disjointed feed-forward network algorithm.…”

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“…Similar to the NNAR model, the multilayer perceptron (MLP) model also uses artifcial neurons to migrate processed information from one layer to another [40,41]. Te hidden layers receive the processed information from the input layers and pass it through an interconnected processed fact in a random ramifcation to the output layers in a manner that will ensue reciprocation of a feedforward system with disjoint layers [19].…”

“…Te hidden layers receive the processed information from the input layers and pass it through an interconnected processed fact in a random ramifcation to the output layers in a manner that will ensue reciprocation of a feedforward system with disjoint layers [19]. Te MLP network function [40,41] is given by…”