Comparative study of artificial neural network versus parametric method in COVID-19 data analysis
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Access Rights
info:eu-repo/semantics/openAccess
Abstract
Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.
Description
Keywords
Reliability function, Maximum likelihood estimation, Artificial neural network, Failure rate function
Journal or Series
Results in Physics
WoS Q Value
Q1
Scopus Q Value
Q2
Volume
38
