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Rough Sets Based on Reducts of Conditional Attributes in Medical Classification of the Diagnosis Status
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2008, June 1 (English)Conference paper (Refereed) Published
Abstract [en]

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, by testing influence of symptom intensity levels on the diagnosis indisputable appearance we select these standards, whose either presence or absence in the patients allows us to add complementary remarks making the classification effects even more readable.

Place, publisher, year, edition, pages
Hong Kong: IEEE Computational Intelligence Society , 2008, June 1.
Keyword [en]
Rough sets, Classification of diagnoses, Reducts of conditional attributes
National Category
Mathematics Mathematical Analysis
URN: urn:nbn:se:bth-8772ISI: 000262974000157Local ID: 978-1-4244-1818-3OAI: diva2:836524
IEEE World Congress on Computational Intelligence 2008
Available from: 2012-09-18 Created: 2008-01-04 Last updated: 2016-09-20Bibliographically approved

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Rakus-Andersson, Elisabeth
MathematicsMathematical Analysis

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