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Mining medical data to obtain fuzzy predicates.

Lecture Notes Comp. Sci. 8649, 103-117 (2014)
Postprint DOI
Open Access Green
as soon as is submitted to ZB.
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Fuzzy Predicates ; Knowledge Discovery ; Medical Data
Reviewing status