Harnessing Pliancy Tree Soft Sets in Heart Diseases for Extracting Beneficial Rules of Association Rules

Abstract

Cardiovascular diseases (CVDs)continue to be the primary cause of mortality, accounting for 
approximately one-third of all fatalities globally. This spawned the proposal of models in several 
studies. Accordingly, this study contributed to diagnosing heart disease through suggesting soft 
diagnosis paradigm. Various techniques have been volunteered for serving the suggested paradigm 
toward achieving its objective. Additionally, this study provided set of contributions. For instance, 
Tree Soft Technique (TrST) is applied for the first time for forming attributes and sub attributes 
of patients into nodes and sub-nodes of Tree to obtain relations between it. Even, the study support 
stakeholders to making accurate decision in mysterious circumstances and in problems with 
incomplete information through Collaborating the utilized techniques of entropy and Technique 
for order of preference by similarity to ideal solution (TOPSIS) in this study with Single Value 
Neutrosophic Sets (SVNSs) forked from neutrosophic uncertainty theory. As well, the relationship 
between sub-attributes which consider antecedent for obtaining consequent of detecting and 
diagnosing through collaborating TrST with association rules. Accordingly, we applied four 
transactions (cases) for obtaining findings of the relations in transactions as listed in Table 13 and 
Table14. 

DOI: 10.5281/zenodo.11151500