Emergency Decision Support System in Cardiovascular Health Using T-spherical q-Rung Linear Diophantine Fuzzy Set and Logistic Differential Evolution

Abstract

In the field of cardiovascular health, the need to make quick decisions in emergency situations is mandatory to save one’s life. During cardiovascular abnormalities, often the patients become unresponsive as the physical and mental conditions become unstable. A meticulous approach that considers various aspects of emergency circumstances is crucial to address these challenges effectively. This paper proposes an effective emergency decision-making method for cardiovascular health using a new T-spherical q-rung linear diophantine fuzzy set (TSqLDFS), logistic differential evolution optimization, and evidential reasoning methodologies. TSqLDFS is employed with a broader scope of bounds for the experts to assess the evaluation values for alternatives corresponding to specified attributes without any restriction. The optimal weights of each attribute are obtained using logistic differential evolution optimization. Then, the aggregated T-spherical q-rung linear diophantine fuzzy values (TSqLDFVs) of each alternative are calculated using evidential reasoning. Subsequently, the score values are evaluated, facilitating the selection of the optimal choice with the highest score. The outcomes of the proposed approach in the context of cardiovascular health have been compared with the existing methods, ensuring its robustness and better performance in medical scenarios.