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

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dc.authoridShafiq, Anum/0000-0001-7186-7216
dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridSindhu, Tabassum/0000-0001-9433-4981
dc.authoridLone, Showkat Ahmad/0000-0001-7149-3314
dc.contributor.authorShafiq, Anum
dc.contributor.authorColak, Andac Batur
dc.contributor.authorSindhu, Tabassum Naz
dc.contributor.authorLone, Showkat Ahmad
dc.contributor.authorAlsubie, Abdelaziz
dc.contributor.authorJarad, Fahd
dc.date.accessioned2024-11-07T13:35:08Z
dc.date.available2024-11-07T13:35:08Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractSince 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.
dc.identifier.doi10.1016/j.rinp.2022.105613
dc.identifier.issn2211-3797
dc.identifier.pmid35600673
dc.identifier.scopus2-s2.0-85130819263
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.rinp.2022.105613
dc.identifier.urihttps://hdl.handle.net/11480/16353
dc.identifier.volume38
dc.identifier.wosWOS:000804942300006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofResults in Physics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectReliability function
dc.subjectMaximum likelihood estimation
dc.subjectArtificial neural network
dc.subjectFailure rate function
dc.titleComparative study of artificial neural network versus parametric method in COVID-19 data analysis
dc.typeArticle

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