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Application of Rough Sets Theory to the Sequential Diagnosis

  • Andrzej Zolnierek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4345)

Abstract

Sequential classification task is typical in medical diagnosis, when the investigations of the patient’s state are repeated several times. Such situation takes place in controlling of the drug therapy efficacy. In this paper the methods of sequential classification using rough sets theory are developed and evaluated. The proposed algorithms, using the set of learning sequences, calculate the lower and upper approximations of the set of proper decision formulas and then use them to make final decision. Depending on the input data different algorithms are derived. Next, all presented algorithms were practically applied in computer-aided recognition of the human acid-base state balance and the results of comparative experimental analysis of in respect of classification accuracy are also presented and discussed.

Keywords

Decision Algorithm Decision Table Fuzzy Relation Decision Attribute Pattern Recognition Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrzej Zolnierek
    • 1
  1. 1.Faculty of Electronics, Chair of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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