Medical diagnosis problems often involve uncertainty, vagueness, and linguistic evaluations provided by experts, which makes accurate decision-making challenging. Therefore, developing advanced fuzzy decision-making frameworks is important for improving diagnostic reliability in healthcare systems. In this study, the q-rung orthopair hexagonal fuzzy set (q-ROHxFS), which generalizes intuitionistic fuzzy sets (IFS), Pythagorean fuzzy sets (PFS), and Fermatean fuzzy sets (FFS), is employed to address multi-attribute group decision-making (MAGDM) problems in medical diagnosis. The evaluations of decision makers regarding lung disease patients are expressed using linguistic variables and subsequently transformed into q-ROHxF numbers. These assessments are aggregated to construct a decision matrix for determining the optimal alternative. Based on the proposed fuzzy environment, three modified multi-criteria decision-making (MCDM) methods, namely q-ROHxF TOPSIS, q-ROHxF COPRAS, and q-ROHxF VIKOR, are developed. In addition, a novel defuzzification technique and a distance measure for q-ROHxF numbers are introduced to effectively rank the alternatives. To validate the effectiveness of the proposed framework, the obtained results are compared with four existing q-ROF based MCDM methods. Furthermore, rank correlation analysis is performed to examine the consistency and reliability of the proposed algorithms. Sensitivity analysis with different values of q is also conducted to demonstrate the stability of the optimal solution. The results indicate that the proposed q-ROHxF MCDM framework provides a flexible and reliable approach for medical diagnosis decision-making under uncertainty.
Mandal et al. (2026) studied this question.