Max-min Fuzzy and soft sets approach in constructing fuzzy soft matrix for medical decision-making during epidemics

Abstract

In the realm of healthcare decision-making amid outbreaks, the application of vague and pliable collections has garnered noteworthy consideration owing to their capacity to manage unpredictability and vagueness. In this study, an exclusive methodology termed Maximum-Minimum-Fuzzy and Soft-Ensembles-Based Fuzzy Pliable Array (MMFSS-FPA) is proposed for healthcare decision-making during outbreaks. The recommended approach amalgamates the notions linked with both imprecise collections and pliable collections, and maximum-minimum operations to formulate a fuzzy pliable array that encapsulates the uncertainties linked with medical data. By taking into account the association values and resolution thresholds, the MMFSS-FPA eases the identification of optimum decisions in epidemic scenarios. The effectiveness of the proposed strategy is depicted through experimental assessment on genuine medical datasets. The findings reveal that the MMFSS-FPA approach attains precise and dependable decision-making with a triumph rate surpassing 90%, establishing it as a promising instrument for healthcare professionals in addressing outbreaks.