Applications of interval-valued pythagorean fuzzy trigonometric similarity measures in multicriteria decision-making and medical diagnosis

Interval-valued Pythagorean fuzzy sets (IVPFSs) serve as a formidable tool for addressing uncertainty in information, resulting in their extensive adoption in the realm of decision making. The challenge of accurately measuring the similarity between IVPFSs remains a pressing issue. In this paper, we present some novel similarity measures based on trigonometric function and their weighted forms considering membership, non-membership and hesitancy degrees, respectively. These similarity measures adhere to key properties, which are demonstrated through numerical experiments. Subsequently, the proposed similarity measures tailored for IVPFSs are employed to address medical diagnosis and multicriteria decision-making (MCDM) problems within the context of interval-valued Pythagorean fuzzy environments. The results conclusively evidence that the proposed similarity measures result in substantially more efficient outcomes.