A Divergence-Based Medical Decision-Making Process of COVID-19 Diagnosis | Zendy

Bahram Farhadinia | Zendy

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A Divergence-Based Medical Decision-Making Process of COVID-19 Diagnosis

Author(s) -

Bahram Farhadinia

Publication year - 2022

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2022/7685033

Subject(s) - divergence (linguistics) , covid-19 , fuzzy logic , parametric statistics , fuzzy set , process (computing) , computer science , data mining , artificial intelligence , mathematics , statistics , medicine , philosophy , linguistics , disease , pathology , infectious disease (medical specialty) , operating system

This study is concerned with introducing a class of parametric and symmetric divergence measures under hesitant fuzzy environment. The proposed divergence measures have several interesting properties which make their use attractive. In order for exploring the features of proposed divergence measures for hesitant fuzzy sets (HFSs), we compare them with other existing ones in terms of divergence-initiated weighs and counter-intuitive cases. In the process of comparison, we first modify the conventional framework of hesitant fuzzy additive ratio assessment (HFARAS) using the proposed divergence measures, and then, the superiority of proposed measures is further demonstrated in a COVID-19 case study. There, we notify that the other existing divergence measures may not provide satisfactory results.

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