Advanced Decision-Making With Complex q-Rung Orthopair Fuzzy Muirhead Mean Models

Abrar Hussain; Chaudhary Amir Raza; Kifayat Ulllah

doi:10.31181/sdmap31202644

Authors

Abrar Hussain Department of Mathematics, Riphah International University (Lahore Campus), 54000, Lahore, Pakistan Author https://orcid.org/0000-0003-2289-7464
Chaudhary Amir Raza Department of Mathematics, Riphah International University (Lahore Campus), 54000, Lahore, Pakistan Author https://orcid.org/0009-0008-3436-743X
Kifayat Ulllah 1) Department of Mathematics, Riphah International University (Lahore Campus), 54000, Lahore, Pakistan; 2) Department of Mathematics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai 602105, Tamil Nadu, India Author https://orcid.org/0000-0002-1438-6413

DOI:

https://doi.org/10.31181/sdmap31202644

Keywords:

Complex q-rung orthopair fuzzy values, Aggregation operators, Muirhead mean, MCDM, Multicriteria decision-making

Abstract

Reliable and adaptable approaches are essential in MADM to address the complexities of medical diagnosis. To enhance decision-making (DM) processes, we propose a novel method combining the Muirhead mean (MM) with q-rung orthopair fuzzy sets (q-ROFS). The q-ROFS framework offers an advanced solution for handling ambiguity and vagueness in medical diagnoses by enabling a three-dimensional representation of expert opinions through membership degrees (MDs). The proposed method leverages MM's strength in capturing interrelationships among features, ensuring a more accurate and balanced integration of expert inputs. This integration is particularly valuable in medical diagnosis, where the interplay and relative importance of symptoms and diagnostic criteria are complex and critical. By expanding the theoretical applications of fuzzy sets (FS) in MADM, this innovative approach not only improves patient outcomes but also enhances the reliability of diagnostic procedures. It provides a practical tool for elevating DM quality in medical settings.

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