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

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.