Constructing a knee osteoarthritis transplant via the neutrality aggregation operators under spherical fuzzy Z-numbers framework

Understanding the biomechanics of osteoarthritis is necessary for designing a biomedical knee implant to reduce pain, increase mobility, and enhance the patient's quality of life. The most appropriate implant design may be chosen by using Multi-Attribute Group Decision-Making (MAGDM) techniques, which include a number of variables including material characteristics, biomechanical performance, cost-effectiveness, and patient-specific requirements. Compared to conventional fuzzy set structures, Spherical Fuzzy $\hat{Z}$ -Number Sets ( $SF\hat{Z}N$ S) provide an enhanced method for resolving uncertainty in MAGDM and are more suited for handling complicated decision-making situations. In order to get crucial weight information, this work uses spherical fuzzy entropy measures ( $\mathrm{EM}$ ) to suggest a unique distance metric specifically designed for spherical fuzzy $\hat{Z}$ -numbers. Furthermore, to solve issues in MAGDM, especially when the preferences and criterion weights of decision-makers are uncertain, we offer Aggregation Operators based on Neutrality Aggregation ( $\mathrm{NA}$ ) T-norm and T-Conorm. We first investigate the $SF\hat{Z}N$ notion, their accuracy and scoring functions, and the fundamental ideas that underlie their work. Next, we create a method for generating decisions for MAGDM situations by using $SF\hat{Z}N$ information. Our suggested operators provide a flexible and resilient structure for collecting information by integrating $\mathrm{NA}$ operations into the $SF\hat{Z}N$ framework. This makes them particularly useful for developing knee implants for osteoarthritis. This work advances the field of MAGDM for biomedical engineering both theoretically and practically. We provide comparison findings to confirm our technique and show its efficacy and field usefulness.