Mining Medical Data to Obtain Fuzzy Predicates

Abstract

The collection of methods known as "data mining" offers methodological and technical solutions to deal with the analysis of medical data and the construction of models. Medical data have a special status based upon their applicability to all people; their urgency (including life-or death); and a moral obligation to be used for beneficial purposes. Due to this reality, this article addresses the special features of data mining with medical data. Specifically, we will apply a recent data mining algorithm called FuzzyPred. It performs an unsupervised learning process to obtain a set of fuzzy predicates in a normal form, specifically conjunctive (CNF) and disjunctive normal form (DNF). Experimental studies in known medical datasets shows some examples of knowledge that can be obtained by using this method. Several kind of knowledge that was obtained by FuzzyPred in these databases cannot be obtained by other popular data mining techniques.

Mining predicate rules without minimum support threshold

“…by the proposed algorithm are novel and the experiments showed that the traditional ARM techniques were unable to discover these rules. Predicate logic in combination with fuzzy logic has been studied by Ceruto et al (2014) and proposed an algorithm called FuzzyPred and implemented on multiple medical datasets. The proposed algorithm obtains patterns through fuzzy predicates representing the dependence between items in the datasets.…”

Impact of Fuzzy Logic in Object-Oriented Database Through Blockchain

“…Sharma et al [7] have discussed potential future instances for casual association examination research as well as data mining approaches and the subject of relational connection assessment. For the most advantageous aspects of employing clinical data for data mining, Ceruto et al [8] have kept a close eye. We will use the Fluffy red information mining calculation in particular.…”