NDLI: Rough set techniques for medical diagnosis systems

Content Provider	IEEE Xplore Digital Library
Author	Ilczuk, G. Mlynarski, R. Wakulicz-Deja, A. Drzewiecka, D. Kargul, W.
Copyright Year	2005
Description	Author affiliation: HEITEC AG Systemhaus fuer Automatisierung und Informationstechnol., Erlangen (Ilczuk, G.)
Abstract	The process of discovering natural phenomena or complex system was until recently limited to finding formulas that fit empirical data. This process used with success in science and engineering has its limits when the complexity of the natural processes increases. Therefore, to analyze data in the medical domain an alternative approach was needed. Several mathematical methods including neural nets, inductive learning fuzzy sets and rough sets were proposed to model data in the form of decision tables or rules. Pawlak's rough sets theory for handling imprecision and uncertainty in data has a main advantage over the other techniques which is the possibility to analyze data without any preliminary information what favor its usage in medical decision systems. In this paper we present the results of rule generation from our implementation of the LEM2 algorithm on reduced sets of attributes calculated using the Wrapper method with different learning algorithms
Starting Page	837
Ending Page	840
File Size	79954
Page Count	4
File Format	PDF
ISBN	0780393376
DOI	10.1109/CIC.2005.1588235
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-09-25
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Medical diagnosis Data analysis Medical diagnostic imaging Rough sets Biomedical engineering Neural networks Fuzzy sets Mathematical model Uncertainty Information analysis
Content Type	Text
Resource Type	Article

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