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Methods for Detecting COVID-19 Patients Using Interval-Valued T-Spherical Fuzzy Relations and Information Measures

Author

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  • Yinyu Wang

    (College of Economic and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, P. R. China)

  • Kifayat Ullah

    (Department of Mathematics, Riphah International University Lahore Campus, Lahore 54000, Pakistan)

  • Tahir Mahmood

    (Department of Mathematics and Statistics, International Islamic University Islamabad Islamabad, 44000, Pakistan)

  • Harish Garg

    (School of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala 147004, Punjab, India)

  • Lemnaouar Zedam

    (Laboratory of Pure and Applied Mathematics, Department of Mathematics, Med Boudiaf University of Msila, Ichbilia, Msila 28000, Algeria)

  • Shouzhen Zeng

    (School of Business, Ningbo University, Ningbo 315100, P. R. China)

  • Xingsen Li

    (Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou 510006, P. R. China)

Abstract

The concepts of relations and information measures have importance whenever we deal with medical diagnosis problems. The aim of this paper is to investigate the global pandemic COVID-19 scenario using relations and information measures in an interval-valued T-spherical fuzzy (IVTSF) environment. An IVTSF set (IVTSFS) allows describing four aspects of human opinions i.e., membership, abstinence, non-membership, and refusal grade that process information in a significant way and reduce information loss. We propose similarity measures and relations in the IVTSF environment and investigate their properties. Both information measures and relations are applied in a medical diagnosis problem keeping in view the global pandemic COVID-19. How to determine the diagnosis based on symptoms of a patient using similarity measures and relations is discussed. Finally, the advantages of dealing with such problems using the IVTSF framework are demonstrated with examples.

Suggested Citation

  • Yinyu Wang & Kifayat Ullah & Tahir Mahmood & Harish Garg & Lemnaouar Zedam & Shouzhen Zeng & Xingsen Li, 2023. "Methods for Detecting COVID-19 Patients Using Interval-Valued T-Spherical Fuzzy Relations and Information Measures," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1033-1060, May.
    • Handle: RePEc:wsi:ijitdm:v:22:y:2023:i:03:n:s0219622022500122
    DOI: 10.1142/S0219622022500122
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    Cited by:

    1. Wang, Weizhong & Chen, Yu & Zhang, Tinglong & Deveci, Muhammet & Kadry, Seifedine, 2024. "The use of AI to uncover the supply chain dynamics of the primary sector: Building resilience in the food supply chain," Structural Change and Economic Dynamics, Elsevier, vol. 70(C), pages 544-566.

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