@article{Asaad2026, 
author = {Baravan A. Asaad and Sagvan Y. Musa and Badr Alharbi and Zanyar A. Ameen},
title = {Advancing smart healthcare decision-making: an innovative Fermatean fuzzy N-bipolar soft expert set framework for complex multi-criteria group evaluations},
year = {2026},
journal = {AIMS Mathematics},
volume = {11},
number = {1},
pages = {1071-1116},
keywords = {Fermatean fuzzy N-bipolar soft expert sets, multi-criteria group decision-making, smart healthcare, soft expert sets, N-soft sets, Fermatean fuzzy sets},
url = {https://www.sciopen.com/article/10.3934/math.2026047},
doi = {10.3934/math.2026047},
abstract = {The rapid advancement of smart healthcare systems demands sophisticated mathematical tools to manage uncertainty and conflicting expert opinions in critical decision-making (DM) processes. Multi-criteria group decision making (MCGDM) plays a pivotal role in synthesizing diverse expert evaluations for complex healthcare challenges. However, existing soft set (SS) extensions often struggle to simultaneously capture multinary evaluations, bipolar reasoning, higher-order fuzzy logic, and multi-expert input. To overcome these limitations, we propose the Fermatean fuzzy N-bipolar soft expert set (FFNBSES), which enhances fuzzy representation while integrating multinary, bipolar, and multi-expert evaluations. We formally define the fundamental operations of FFNBSES and demonstrate its algebraic properties. A DM methodology based on FFNBSES is developed and applied to a healthcare case study, showcasing its superior capability to handle expert consensus and disagreement in multi-criteria evaluations. Comparative analysis within the SS theory framework highlights the enhanced flexibility and robustness of FFNBSES for real-world MCGDM problems. This work provides a powerful and comprehensive approach to support smart healthcare transformation through improved group DM under uncertainty.}
}