Selection of Optimal Approach for Cardiovascular Disease Diagnosis under Complex Intuitionistic Fuzzy Dynamic Environment

Dilshad Alghazzawi, Maryam Liaqat, Abdul Razaq (), Hanan Alolaiyan, Umer Shuaib () and Jia-Bao Liu
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Dilshad Alghazzawi: Department of Mathematics, College of Science & Arts, King Abdul Aziz University, Rabigh 25732, Saudi Arabia
Maryam Liaqat: Department of Mathematics, Division of Science and Technology, University of Education, Lahore 54770, Pakistan
Abdul Razaq: Department of Mathematics, Division of Science and Technology, University of Education, Lahore 54770, Pakistan
Hanan Alolaiyan: Department of Mathematics, King Saud University, Riyadh 145111, Saudi Arabia
Umer Shuaib: Department of Mathematics, Government College University, Faisalabad 38000, Pakistan
Jia-Bao Liu: School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, China

Mathematics, 2023, vol. 11, issue 22, 1-23

Abstract: Cardiovascular disease (CVD) is a leading global health concern. There is a critical need for accurate and reliable decision-making tools to select the optimal approach for diagnosing cardiovascular disease (CVD). In this study, we have addressed this pressing issue. Complex intuitionistic fuzzy set (CIFS) theory is adept at encapsulating vagueness due to its capability to encompass comprehensive problem specifications characterized by both intuitionistic uncertainty and periodicity. Within the scope of this article, we present two novel aggregation operators: the complex intuitionistic fuzzy dynamic weighted averaging (CIFDWA) operator and the complex intuitionistic fuzzy dynamic weighted geometric (CIFDWG) operator. Some intriguing characteristics of these operators are elucidated, and important special cases are also defined in detail. We devise an enhanced score function to rectify the deficiencies observed in the existing score function under complex intuitionistic fuzzy knowledge. Furthermore, these operators are employed in the development of a systematic approach for the handling of multiple attribute decision-making (MADM) scenarios involving complex intuitionistic fuzzy data. Moreover, we undertake the resolution of an MADM problem, wherein we ascertain the optimal approach for diagnosing cardiovascular disease (CVD) through the utilization of the proposed operators, thereby substantiating their utility in decision-making processes. Finally, we conduct a comprehensive comparative analysis, pitting the presented operators against an array of existing counterparts, in order to demonstrate the reliability and stability inherent in the derived methodologies.

Keywords: complex intuitionistic fuzzy sets; dynamic aggregation operators; decision-making methods; cardiovascular disease (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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