NDLI: Rough sets based on reducts of conditional attributes in medical classification of the diagnosis status

Content Provider	IEEE Xplore Digital Library
Author	Rakus-Andersson, E.
Copyright Year	2008
Description	Author affiliation: Dept. of Math. & Sci., Blekinge Inst. of Technol., Karlskrona (Rakus-Andersson, E.)
Abstract	Rough sets constitute helpful mathematical tools of the classification of objects belonging to a certain universe when dividing the universe in two collections filled with sure and possible members. In this work we adopt the rough technique to verify diagnostic decisions concerning a sample of patients whose symptoms are typical of a considered diagnosis. The objective is to extract the patients who surely suffer from the diagnosis, to indicate the patients who are free from it, and even to make decisions in undefined diagnostic cases. We also consider a decisive power of reducts being minimal collections of symptoms, which preserve the previous classification results. We use them in order to minimize a number of numerical calculations in the classification process. Finally, we test influence of symptom intensity levels on the diagnosis indisputable appearance to select these levels that are expected to be found in patients suffering from the considered diagnosis. The presence or the absence of these symptom levels in the patients allow us to add complementary remarks to earlier classification effects making them even more readable.
Starting Page	991
Ending Page	998
File Size	282485
Page Count	8
File Format	PDF
ISBN	9781424418183
ISSN	10987584
DOI	10.1109/FUZZY.2008.4630490
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy systems Conferences
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Theoretical Computer Science Software

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