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An Optimized Decision Support Model for COVID-19 Diagnostics Based on Complex Fuzzy Hypersoft Mapping

Author

Listed:
  • Muhammad Saeed

    (Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan)

  • Muhammad Ahsan

    (Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan)

  • Muhammad Haris Saeed

    (Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan)

  • Atiqe Ur Rahman

    (Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan)

  • Asad Mehmood

    (Department of Mathematics, University of Management and Technology, Lahore 54000, Pakistan)

  • Mazin Abed Mohammed

    (College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq)

  • Mustafa Musa Jaber

    (Department of Computer Science, Dijlah University College, Baghdad 00964, Iraq
    Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq)

  • Robertas Damaševičius

    (Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland)

Abstract

COVID-19 has shaken the entire world economy and affected millions of people in a brief period. COVID-19 has numerous overlapping symptoms with other upper respiratory conditions, making it hard for diagnosticians to diagnose correctly. Several mathematical models have been presented for its diagnosis and treatment. This article delivers a mathematical framework based on a novel agile fuzzy-like arrangement, namely, the complex fuzzy hypersoft ( CFHS ) set, which is a formation of the complex fuzzy (CF) set and the hypersoft set (an extension of soft set). First, the elementary theory of CFHS is developed, which considers the amplitude term (A-term) and the phase term (P-term) of the complex numbers simultaneously to tackle uncertainty, ambivalence, and mediocrity of data. In two components, this new fuzzy-like hybrid theory is versatile. First, it provides access to a broad spectrum of membership function values by broadening them to the unit circle on an Argand plane and incorporating an additional term, the P-term, to accommodate the data’s periodic nature. Second, it categorizes the distinct attribute into corresponding sub-valued sets for better understanding. The CFHS set and CFHS -mapping with its inverse mapping (INM) can manage such issues. Our proposed framework is validated by a study establishing a link between COVID-19 symptoms and medicines. For the COVID-19 types, a table is constructed relying on the fuzzy interval of [ 0 , 1 ] . The computation is based on CFHS -mapping, which identifies the disease and selects the optimum medication correctly. Furthermore, a generalized CFHS -mapping is provided, which can help a specialist extract the patient’s health record and predict how long it will take to overcome the infection.

Suggested Citation

  • Muhammad Saeed & Muhammad Ahsan & Muhammad Haris Saeed & Atiqe Ur Rahman & Asad Mehmood & Mazin Abed Mohammed & Mustafa Musa Jaber & Robertas Damaševičius, 2022. "An Optimized Decision Support Model for COVID-19 Diagnostics Based on Complex Fuzzy Hypersoft Mapping," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2472-:d:863916
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    References listed on IDEAS

    as
    1. Sagvan Y. Musa & Baravan A. Asaad, 2021. "Bipolar Hypersoft Sets," Mathematics, MDPI, vol. 9(15), pages 1-15, August.
    2. Faruk Karaaslan, 2016. "Soft Classes and Soft Rough Classes with Applications in Decision Making," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-11, February.
    3. D. Ajay & J. Joseline Charisma & N. Boonsatit & P. Hammachukiattikul & G. Rajchakit & Broumi Said, 2021. "Neutrosophic Semiopen Hypersoft Sets with an Application to MAGDM under the COVID-19 Scenario," Journal of Mathematics, Hindawi, vol. 2021, pages 1-16, April.
    4. Athar Kharal & B. Ahmad, 2011. "Mappings On Soft Classes," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 7(03), pages 471-481.
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    Cited by:

    1. Abdellah Chehri & Francois Rivest, 2023. "Editorial for the Special Issue “Advances in Machine Learning and Mathematical Modeling for Optimization Problems”," Mathematics, MDPI, vol. 11(8), pages 1-5, April.

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