Capacity Evaluation of Diagnostic Tests For COVID-19 Using Multicriteria Decision-Making Techniques

Sayan, Murat; Yıldırım, Figen; Şanlıdağ, Tamer; Uzun, Berna; Ozsahin, Dilber Uzun; Ozsahin, Ilker

doi:10.1155/2020/1560250

Cited by 28 publications

(31 citation statements)

References 31 publications

(40 reference statements)

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“…Although logistic regression can determine the key indicator Age, and discrete analysis can find a better subset {Age, WBC LYMC}, it is difficult to determine the best subset due to the small sample size or multicollinearity. Previous studies used the MCDM algorithm to evaluate diagnostic tests [ 46 ] and help doctors hasten COVID-19 treatment [ 47 ]. As far as we know, this is the first time the MCDM algorithm has been used to predict the severity of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

et al. 2021

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The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients’ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44%, {Age, NEUT, LYMC} with a selection rate of 38%, and {Age, WBC, LYMC} with a selection rate of 9%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators.

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Section: Discussionmentioning

confidence: 99%

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

et al. 2021

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“…MCDM techniques can be used to solve problems related to the COVID-19 pandemic in health ( Majumder et al, 2020 , Sayan et al, 2020 , Albahri et al, 2020 , Shirazi et al, 2020 ), education (Alqahtan and Rajkhan, 2020), and various fields ( Vinodhini, 2020 , Korzeb and Niedziółka, 2020 ). The purpose and the methods used for some of these studies are summarized and presented in the subsequent paragraphs.…”

Section: Methodologiesmentioning

confidence: 99%

“… Sayan et al (2020) compared the existing SARS-CoV-2 diagnostic tests and determined the most effective test among them through enrichment evaluation (fuzzy PROMETHEE) and fuzzy TOPSIS methods.…”

Section: Methodologiesmentioning

confidence: 99%

Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment

Hezer

Gelmez

Özceylan

2021

Journal of Infection and Public Health

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COVID-19, which emerged in December 2019, has affected the entire world. Therefore, COVID-19 has been a subject of research in various disciplines, especially in the field of health. One of these studies was the report made by the Deep Knowledge Group (DKG) consortium in which safe regions for COVID-19 were determined. In the report, the main criteria of quarantine efficiency, government efficiency of risk management, monitoring and detection, health readiness, regional resilience, and emergency preparedness are used in the evaluation of countries and regions (alternatives). As the data and research structure used in this report are based on multi-criteria, the purpose of this study is to evaluate and analyse the safety levels of 100 regions in the world in terms of COVID-19 using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) and Complex Proportional Assessment (COPRAS) methods. The data and information required in the methods were obtained from a report prepared by the DKG. The results of the methods were compared with the ranking results presented in a report of the DKG. Accordingly, it has been observed that the method that provides the closest results to the results of the report is the COPRAS method, and the method that gives the most distant results is the VIKOR method.

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“…Zolfani et al ( 2020 ) applied a novel Grey‐based decision framework to select the best location for a temporary Covid‐19 hospital. Sayan et al ( 2020 ) compared the results of fuzzy‐based TOPSIS and PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) methods which are applied in the evaluations of Covid‐19 diagnostic tests. Yang et al ( 2020 ) proposed spherical fuzzy aggregation operators and their applications are shown on the antivirus mask selection problem.…”

Section: An Applicationmentioning

confidence: 99%

Fermatean fuzzy set extensions of SAW, ARAS, and VIKOR with applications in COVID‐19 testing laboratory selection problem

Gül

2021

Expert Systems

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The multiple attribute decision‐making models are empowered with the support of fuzzy sets such as intuitionistic, q‐rung orthopair, Pythagorean, and picture fuzzy sets, and also neutrosophic sets, etc. These concepts generate varying representation opportunities for the decision‐maker's preferences and expertise. Pythagorean and Fermatean fuzzy sets are special cases of q‐rung orthopair fuzzy set when q = 2 and q = 3, respectively. From a geometric perspective, the latter provides a broader representation domain than the former does. In this study, the emerging concept of Fermatean fuzzy set is studied in detail and three well‐known multi‐attribute evaluation methods, namely SAW, ARAS, and VIKOR are extended under Fermatean fuzzy environment. In this manner, the decision‐makers will have more freedom in specifying their preferences, thoughts, and expertise, and the abovementioned decision approaches will be able to handle this new type of data. The applicability of the propositions is shown in determining the best Covid‐19 testing laboratory which is an important topic of the ongoing global health crisis. To validate the proposed methods, a benchmark analysis covering the results of the existing Fermatean fuzzy set‐based decision methods, namely TOPSIS, WPM, and Yager aggregation operators is presented.

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Capacity Evaluation of Diagnostic Tests For COVID-19 Using Multicriteria Decision-Making Techniques

Cited by 28 publications

References 31 publications

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity

Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment

Fermatean fuzzy set extensions of SAW, ARAS, and VIKOR with applications in COVID‐19 testing laboratory selection problem

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