Elsevier

Results in Physics

Volume 38, July 2022, 105613
Results in Physics

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis

https://doi.org/10.1016/j.rinp.2022.105613Get rights and content
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Highlights

  • Real application has been developed for estimating COVID-19 mortality rates.

  • Artificial neural network modeling and maximum likelihood estimation.

  • Predict and analyze the reliability features of the EOWR model.

  • ANN model with Levenberg-Marquardt shows the highest prediction performance.

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.

Keywords

Reliability function
Maximum likelihood estimation
Artificial neural network
Failure rate function

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