A strategy for hepatitis diagnosis by using spherical $ q $-linear Diophantine fuzzy Dombi aggregation information and the VIKOR method

Huzaira Razzaque; Shahzaib Ashraf; Wajdi Kallel; Muhammad Naeem; Muhammad Sohail; Huzaira Razzaque; Shahzaib Ashraf; Wajdi Kallel; Muhammad Naeem; Muhammad Sohail

doi:10.3934/math.2023735

AIMS Mathematics

2023, Volume 8, Issue 6: 14362-14398. doi: 10.3934/math.2023735

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Research article

A strategy for hepatitis diagnosis by using spherical $ q $-linear Diophantine fuzzy Dombi aggregation information and the VIKOR method

1.
Institute of Mathematics, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Punjab, Pakistan
2.
Department of Mathematics, Faculty of Applied sciences, Umm Al-Qura University, Holly Makkah 21955, Saudi Arabia

Received: 16 February 2023 Revised: 15 March 2023 Accepted: 23 March 2023 Published: 19 April 2023
MSC : 03E72, 47S40

Hepatitis is an infectious disease typified by inflammation in internal organ tissues, and it is caused by infection or inflammation of the liver. Hepatitis is often feared as a fatal illness, especially in developing countries, mostly due to contaminated water, poor sanitation, and risky blood transfusion practices. Although viruses are typically blamed, other potential causes of this kind of liver infection include autoimmune disorders, toxins, medicines, opioids, and alcohol. Viral hepatitis may be diagnosed using a variety of methods, including a physical exam, liver surgery (biopsy), imaging investigations like an ultrasound or CT scan, blood tests, a viral serology panel, a DNA test, and viral antibody testing. Our study proposes a new decision-support system for hepatitis diagnosis based on spherical q-linear Diophantine fuzzy sets (Sq-LDFS). Sq-LDFS form the generalized structure of all existing notions of fuzzy sets. Furthermore, a list of novel Einstein aggregation operators is developed under Sq-LDF information. Also, an improved VIKOR method is presented to address the uncertainty in analyzing the viral hepatitis categories demonstration. Interesting and useful properties of the proposed operators are given. The core of this research is the proposed algorithm based on the proposed Einstein aggregation operators and improved VIKOR approach to address uncertain information in decision support problems. Finally, a hepatitis diagnosis case study is examined to show how the suggested approach works in practice. Additionally, a comparison is provided to demonstrate the superiority and efficacy of the suggested decision technique.
- Sq-LDFS,
- Dombi aggregation operators,
- VIKOR technique,
- hepatitis diagnosis,
- decision-making
Citation: Huzaira Razzaque, Shahzaib Ashraf, Wajdi Kallel, Muhammad Naeem, Muhammad Sohail. A strategy for hepatitis diagnosis by using spherical $ q $-linear Diophantine fuzzy Dombi aggregation information and the VIKOR method[J]. AIMS Mathematics, 2023, 8(6): 14362-14398. doi: 10.3934/math.2023735

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Abstract

Hepatitis is an infectious disease typified by inflammation in internal organ tissues, and it is caused by infection or inflammation of the liver. Hepatitis is often feared as a fatal illness, especially in developing countries, mostly due to contaminated water, poor sanitation, and risky blood transfusion practices. Although viruses are typically blamed, other potential causes of this kind of liver infection include autoimmune disorders, toxins, medicines, opioids, and alcohol. Viral hepatitis may be diagnosed using a variety of methods, including a physical exam, liver surgery (biopsy), imaging investigations like an ultrasound or CT scan, blood tests, a viral serology panel, a DNA test, and viral antibody testing. Our study proposes a new decision-support system for hepatitis diagnosis based on spherical q-linear Diophantine fuzzy sets (Sq-LDFS). Sq-LDFS form the generalized structure of all existing notions of fuzzy sets. Furthermore, a list of novel Einstein aggregation operators is developed under Sq-LDF information. Also, an improved VIKOR method is presented to address the uncertainty in analyzing the viral hepatitis categories demonstration. Interesting and useful properties of the proposed operators are given. The core of this research is the proposed algorithm based on the proposed Einstein aggregation operators and improved VIKOR approach to address uncertain information in decision support problems. Finally, a hepatitis diagnosis case study is examined to show how the suggested approach works in practice. Additionally, a comparison is provided to demonstrate the superiority and efficacy of the suggested decision technique.

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