Advancement Through Research
Rough Set theory (RST) is a mathematical tool and used to deal with vagueness, impreciseness, inconsistence and uncertain type knowledge. RST-based research has been applied in machine learning, inductive reasoning, decision support systems and knowledge discovery applications. Popular methods like finding of reducts, core, feature selection and reduction through the concepts of approximations have attracted researchers to use RST further in the field of high dimensional data like social networks, IoT applications and Big data analytics. In this article we make an attempt to summarize the basic concepts, characteristics of RST, some evolutionary extensions of RST and applications limited to Medical data analysis. In keeping the view of learners, a survey on RST based software tools and packages outlined with their exhaustive functionalities. It also identifies the importance of RST in the domain of medical or clinical data analytics, and also exhibits the strengths and limitations of the respective underlying approaches.
Rough Set, Reduct, Core, Medical Data Analytics, Clinical Dataset
