Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices

Zeynep Parla Parmaksız; Burak Arslan; Samet Memiş; Serdar Enginoğlu

doi:10.53570/jnt.1128289

Araştırma Makalesi

Diagnosing COVID-19, Prioritizing Treatment, and Planning Vaccination Priority via Fuzzy Parameterized Fuzzy Soft Matrices

Yıl 2022, Sayı: 39, 54 - 83, 30.06.2022

Zeynep Parla Parmaksız , Burak Arslan , Samet Memiş , Serdar Enginoğlu

https://doi.org/10.53570/jnt.1128289

Cited By: 3

Öz

In the fight against the COVID-19 pandemic, it is vital to rapidly diagnose possible contagions, treat patients, plan follow-up procedures with correct and effective use of resources and ensure the formation of herd immunity. The use of machine learning and statistical methods provides great convenience in dealing with too many data produced during research. Since access to the PCR test used for the diagnosis of COVID-19 may be limited, the test is relatively too slow to yield results, the cost is high, and its reliability is controversial; thus, making a symptomatic classification before the PCR is timesaving and far less costly. In this study, by modifying a state-of-the-art classification method, namely Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC), an effective method is developed for a rapid diagnosis of COVID-19. The paper then presents the accuracy, sensitivity, specificity, and F1-score values that represent the diagnostic performances of the modified method. The results show that the modified method can be adopted as a competent and accurate diagnosis procedure. Afterwards, a tirage study is performed by calculating the patients’ risk scores to manage inpatient overcrowding in healthcare institutions. In the subsequent section, a vaccine priority algorithm is proposed to be used in the case of a possible crisis until the supply shortage of a newly developed vaccine is over if a possible variant of COVID-19 that is highly contagious is insensitive to the vaccine. The accuracy of the algorithm is tested with real-life data. Finally, the need for further research is discussed.

Anahtar Kelimeler

Medical diagnosing, prioritizing treatment, planning vaccination priority, fpfs-matrices, soft decision-making

Destekleyen Kurum

TUBİTAK

Proje Numarası

1689B012131957

Teşekkür

The authors thank Beşiktaş Arts and Sciences Centre and The Scientific and Technological Research Council of Turkey (TUBİTAK) for their valuable support. This study was presented at Regeneron International Science and Engineering Fair (ISEF) 2022 by Zeynep Parla Parmaksız.

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