Integration of FDOSM and FWZIC Under Homogeneous Fermatean Fuzzy Environment: A Prioritization of COVID-19 Patients for Mesenchymal Stem Cell Transfusion

Autor: H. A. Alsattar, Sara Qahtan, R. T. Mohammed, A. A. Zaidan, O. S. Albahri, Gang Kou, A. H. Alamoodi, A. S. Albahri, B. B. Zaidan, Mohammed S. Al-Samarraay, R. Q. Malik, Ali Najm Jasim
Rok vydání: 2022
Předmět:
Zdroj: International Journal of Information Technology & Decision Making. :1-41
ISSN: 1793-6845
0219-6220
Popis: Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-attribute decision-analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist.
Databáze: OpenAIRE
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