Realworld applications are now dealing with a huge amount of data, especially in the area of highdimensional features. Trait reduction is one of the major steps in decision making problems. It refers to the determination of a minimum subset of attributes which preserves the final decision based on the entire set of attributes. Unfortunately, most of the current features are irrelevant or redundant, which makes these systems unreliable and imprecise. This paper proposes a new paradigm based on fuzzy soft relationship and level fuzzy soft relationship, called Union  Intersection decision making method. Using these new principles, the decisionmaking strategy is structured to choose a fuzzy set of optimal elements from the alternatives on the basis of a fuzzy soft set. Finally, we used our proposed method in medical application to make the decision to diagnose COVID19. Moreover, we used MATLAB programming to obtain the results; this has coincided with the announcement by the World Health Organization and an accurate proposal was examined, which competes with that of the method of Zhao.
The most recent Coronavirus (COVID19) epidemic was an outbreak in China at the start of 2020. China increased its national publichealth response to the highest level on January 23, 2020. A variety of initiatives have introduced public social distancing to reduce the rate of COVID19 transmission as part of the emergency response [
In diverse research areas, the mathematical modeling of vagueness and ambiguity has become an increasingly important topic. For many decades, people when making and taking their own decisions usually depend on the results of analyzing the available data about their problems of interest [
The concepts of the core and the reduced are two central concepts of the rough set theory in the case of attributes and knowledge [
Decision making plays a critical role in our everyday lives, and among multiple alternatives, this mechanism offers the best alternative. There exist several decision making applications, such as ([
The main contribution in the current work is to introduce a new concept based on fuzzy soft relation with level fuzzy soft relation called UnionIntersection decision making method and it enables us to determine the optimal traits or elements most affected by the disease.
The main contributions of this paper are as follows:
Finally, we illustrate the importance of the proposed method in medical science for application in decision making problems. In fact, a medical application in decision making for information system of medical diagnosis of COVID19 disease is presented with algorithm. In general, we believe that this work provides a readable framework that will be useful for medical fields that rely on decision making according to a set of symptoms on a specific group of patients, such as the application of COVID19. The relation between the attributes and the knowledge of any attribute of significance in the Coronavirus epidemic (COVID19) was also explained. We would like to note that the sample was composed of 1000 patients from whom the information was gathered for this research on coronavirus. This was investigated using the various methods described in the analysis. Finally, we also used the algorithms to get the basic symptoms casing of the COVID19 coating of a sample of patients. These results may help the physician in making the best decision.
The paper is structured as follows: The basic concepts of the rough set theory and fuzzy set were explored in section two. The implementation of COVID19 for each subclass of attributes in the information systems and comparative analysis was presented in section three. Section four concludes and highlights future scope.
The incomplete technology of modern mathematical knowledge, i.e., ambiguity, is included in the fuzzy and rough sets. Fuzzy set works on the data features while rough set works on an attribute set of the data. Therefore, the approximation of fuzzy input in the crisp approximation space is the consequence of roughfuzzy hybridization set.
In 1982, in order to deal with vagueness in knowledgebased systems, information systems and data dissection, Pawlak [
i) Information systems (IS) is a pair (
ii) The equivalence class
iii) Let IS = (
In this part, we focus on developing a Union  Intersection method based on fuzzy soft relationship.
We will introduce the fuzzy soft relationships and intersection functions of Union  Intersection operators below. Here, as an example, we choose the
where
We implement the proposed method in this application [
Taking the following knowledge system into account in
Patients  Serious symptoms  Most common symptoms  Decision  

Difficulty breathing  Chest pain  High Temperature  Dry cough  Headache  Loss of taste or smell  
yes  yes  v. high  yes  yes  yes  yes  
yes  yes  high  yes  yes  yes  yes  
yes  yes  normal  yes  no  yes  no  
yes  yes  normal  no  no  no  no  
yes  yes  normal  yes  no  no  no  
yes  no  high  yes  yes  no  yes  
no  no  v. high  yes  yes  no  yes  
no  no  normal  yes  yes  no  no  
no  no  v. high  no  no  yes  yes  
no  no  high  yes  yes  no  yes 
We rewrite
Objects  Attributes  Decision  

d  

1  1  
1  
1  
1  

1  
1  
1 
Next in
Leave
1  1  
1  
1  
1  
1  
1  
1  1 
function [core] = core_attributes_one_removal(M); 
Using the Algorithm 1, and
Then, we get the removal of attributes as in
Removal of attributes  

Number of Basic Sets  None  
10  9  10  7  9  10  9 
Decision  

d  

1  1  
1  
1  
1  

1  
1  
1 
Removed attribute
For
When we remove the attribute
1  1  
1  
1  
1  
1  1 
Also, we present
 

1  1  
1  
–  1  
–  –  –  
–  –  –  
–  
1  –  
1  –  1 
Removed attribute
1  
1 
When we remove the attribute
1  
–  –  
–  
–  –  
–  –  1 
The following
1  1  1  1  1  1  1  
–  –  –  –  1  –  –  
–  –  –  –  1  1  –  
–  –  –  –  –  –  –  –  –  
–  –  –  –  –  –  –  –  – 
Also, we find the intersection and union function for
Then, we find the result of the function Union  Intersection of
It is clear that we have identified the main symptoms of COVID19, which are
The present paper represents the Cartesian product of two fuzzy soft sets. We also have a description of the fuzzy soft relationship extending to the level of fuzzy soft relationship, and to the Union  Intersection fuzzy soft relationship. Union  Intersection making method based on a fuzzy soft set is constructed since this technique allows any expert to make a decision about any real problem. Additionally, our approach has provided a new insight into the problem of attribute reduction, and also suggested more semantic properties preserved by an attribute reduction. Consequently, our method provides more flexibility to the decisionmaker to choose which is suitable for him. We also obtained a proposed level of accuracy that depends upon the fuzzy soft set, which was found to be better than that of Zhao accuracy [