Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods

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dc.contributor.authorMohammed, Mazin Abed
dc.contributor.authorAbdulkareem, Karrar Hameed
dc.contributor.authorAl-Waisy, Alaa S.
dc.contributor.authorMostafa, Salama A.
dc.contributor.authorAl-Fahdawi, Shumoos
dc.contributor.authorDinar, Ahmed M.
dc.contributor.authorAlhakami, Wajdi
dc.contributor.authorBaz, Abdullah
dc.contributor.authorAl-Mhiqani, Mohammed Nasser
dc.contributor.authorAlhakami, Hosam
dc.contributor.authorArbaiy, Nureize
dc.contributor.authorMaashi, Mashael S.
dc.contributor.authorMutlag, Ammar Awad
dc.contributor.authorGarcía-Zapirain, Begoña
dc.contributor.authorTorre Díez, Isabel de la
dc.date.accessioned2026-03-18T11:48:06Z
dc.date.available2026-03-18T11:48:06Z
dc.date.issued2020-06-08
dc.date.updated2026-03-18T11:48:06Z
dc.description.abstractNowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around the world. Machine learning (ML) is one of intelligent technique that able to automatically predict the event with reasonable accuracy based on the experience and learning process. In the meantime, a rapid number of ML models have been proposed for predicate the cases of COVID-19. Thus, there is need for an evaluation and benchmarking of COVID-19 ML models which considered the main challenge of this study. Furthermore, there is no single study have addressed the problem of evaluation and benchmarking of COVID diagnosis models. However, this study proposed an intelligent methodology is to help the health organisations in the selection COVID-19 diagnosis system. The benchmarking and evaluation of diagnostic models for COVID-19 is not a trivial process. There are multiple criteria requires to evaluate and some of the criteria are conflicting with each other. Our study is formulated as a decision matrix (DM) that embedded mix of ten evaluation criteria and twelve diagnostic models for COVID-19. The multi-criteria decision-making (MCDM) method is employed to evaluate and benchmarking the different diagnostic models for COVID19 with respect to the evaluation criteria. An integrated MCDM method are proposed where TOPSIS applied for the benchmarking and ranking purpose while Entropy used to calculate the weights of criteria. The study results revealed that the benchmarking and selection problems associated with COVID19 diagnosis models can be effectively solved using the integration of Entropy and TOPSIS. The SVM (linear) classifier is selected as the best diagnosis model for COVID19 with the closeness coefficient value of 0.9899 for our case study data. Furthermore, the proposed methodology has solved the significant variance for each criterion in terms of ideal best and worst best value, beside issue when specific diagnosis models have same ideal best value.en
dc.identifier.citationMohammed, M. A., Abdulkareem, K. H., Al-Waisy, A. S., Mostafa, S. A., Al-Fahdawi, S., Dinar, A. M., Alhakami, W., Baz, A., Al-Mhiqani, M. N., Alhakami, H., Arbaiy, N., Maashi, M. S., Mutlag, A. A., Garcia-Zapirain, B., & De La Torre Diez, I. (2020). Benchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methods. IEEE Access, 8, 99115-99131. https://doi.org/10.1109/ACCESS.2020.2995597
dc.identifier.doi10.1109/ACCESS.2020.2995597
dc.identifier.eissn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5513
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subject.otherBenchmarking methodology
dc.subject.otherChest X-rays images
dc.subject.otherCOVID19 diagnostic
dc.subject.otherEntropy
dc.subject.otherMachine learning
dc.subject.otherMulti-criteria decision-making
dc.subject.otherTOPSIS
dc.titleBenchmarking methodology for selection of optimal COVID-19 diagnostic model based on entropy and TOPSIS methodsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage99131
oaire.citation.startPage99115
oaire.citation.titleIEEE Access
oaire.citation.volume8
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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