Medical decision support for dengue shock syndrome using a bipolar linear diophantine fuzzy hypersoft model with trigonometric similarity

A novel approach to trigonometric similarity measure in the framework of bipolar linear diophantine fuzzy hypersoft sets is introduced. It builds upon existing methodologies by integrating cosine and cotangent-based techniques to enhance decision-making in complex environments. The developed methodology incorporates both positive and negative membership degrees, control parameters, and diophantine information within hypersoft structures. The process involves the construction of bipolar linear diophantine fuzzy hypersoft matrices for multiple alternatives, computation of trigonometric similarity scores between these configurations, and ranking based on closeness to ideal criteria. Mathematical properties of the proposed measures are formally validated. The efficiency of the proposed similarity measures is demonstrated through a multi-attribute decision-making problem involving dengue shock syndrome. The model aids haematologists in identifying high-risk patients, improving diagnostic accuracy under uncertainty. This novel framework contributes to early detection strategies and supports clinical decision-making by providing a robust, mathematically grounded similarity evaluation. In the case study involving dengue shock syndrome, the proposed model achieved a similarity precision of 93.5% and ranked high-risk patients with 98% accuracy, outperforming existing cosine and cotangent-based similarity methods.