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Ceruto, T.* ; Lapeira, O.* ; Tonch, A. ; Plant, C. ; Espín, R.A.* ; Rosete, A.*

Mining medical data to obtain fuzzy predicates.

Lect. Notes Comput. Sc. 8649, 103-117 (2014)
Postprint DOI Order publishers version
Open Access Green
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
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
ISBN 978-3-319-10264-1
Conference Title Information Technology in Bio- and Medical Informatics
Conference Date 2. September 2014
Conference Location Munich, Germany
Quellenangaben Volume: 8649, Issue: , Pages: 103-117 Article Number: , Supplement: ,
Publisher Springer
Publishing Place Berlin [u.a.]