Determination of medical emergency via new intuitionistic fuzzy correlation measures based on Spearman's correlation coefficient

Uncertainty in medical diagnosis is the main challenge in medical emergencies (MEs) experienced by triage nurses and physicians in the emergency department (ED). The intuitionistic fuzzy correlation coe ffi cient (IFCC) approach is used to analyze and interpret the relationship between variables in an uncertain environment. Assorted methods that involve applying a correlation coe ffi cient under intuitionistic fuzzy sets (IFSs) were constructed based on Pearson's correlation model with various drawbacks. In this work, we construct two new intuitionistic fuzzy correlation measures (IFCMs) based on Spearman's correlation model. It is demonstrated that the Spearman -based IFCMs are appropriate for measuring correlation coe ffi cients without any drawbacks. In addition, we show that the Spearman -based IFCMs overcome all the shortcomings of the associated IFCC methods. Equally, the Spearman -based IFCMs satisfy the maxims of the correlation coe ffi cient that have been delineated in the classical case of correlation coe ffi cient. Due to the challenges that uncertainty in medical diagnosis pose to MEs and the proficiency of the IFCC approach, we discuss the application of the constructed IFCMs in a triage process for an e ff ective medical diagnosis during an ME. The medical data for the triage process are obtained via a knowledge -based approach. Finally, comparative analyses are carried out to ascertain the validity and authenticity of the developed Spearman -based IFCMs relative to other IFCC approaches.