Topological Models of Rough Sets and Decision Making of COVID-19

The basic methodology of rough set theory depends on an equivalence relation induced from the generated partition by the classification of objects. However, the requirements of the equivalence relation restrict the field of applications of this philosophy. To begin, we describe two kinds of closure operators that are based on right and left adhesion neighbourhoods by any binary relation. Furthermore, we illustrate that the suggested techniques are an extension of previous methods that are already available in the literature. As a result of these topological techniques, we propose extended rough sets as an extension of Pawlak's models. We offer a novel topological strategy for making a topological reduction of an information system for COVID-19 based on these techniques. We provide this medical application to highlight the importance of the offered methodologies in the decision-making process to discover the important component for coronavirus (COVID-19) infection. Furthermore, the findings obtained are congruent with those of the World Health Organization. Finally, we create an algorithm to implement the recommended ways in decision-making.

Automated summary

The basic methodology of rough set theory relies on an equivalence relation induced from the generated partition by object classification. This study presents closure operators based on neighborhood adhesion, extending previous methods. Extended rough sets are proposed as an extension of Pawlak's models, and a topological reduction strategy for COVID-19 information system is implemented using these techniques. The importance of the offered methodologies in discovering important factors for COVID-19 infection is highlighted through a medical application, which aligns with findings from the World Health Organization. Lastly, an algorithm is created to implement the recommended decision-making approaches.