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Markov Chains Pattern Recognition Approach Applied to the Medical Diagnosis Tasks

  • Michal Wozniak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)

Abstract

In many medical decision problems there exist dependencies between subsequent diagnosis of the same patient. Among the different concepts and methods of using “contextual” information in pattern recognition, the approach through Bayes compound decision theory is both attractive and efficient from the theoretical and practical point of view. Paper presents the probabilistic approach (based on expert rules and learning set) to the problem of recognition of state of acid-base balance and to the problem of computer-aided anti-hypertension drug therapy. The quality of obtained classifier are compared to the frquencies of correct classification of three neural nets.

Keywords

Posterior Probability Learning Sequence Pattern Recognition Algorithm Decision Area Conditional Density Function 
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 2005

Authors and Affiliations

  • Michal Wozniak
    • 1
  1. 1.Chair of Systems and Computer NetworksWroclaw University of TechnologyWroclawPoland

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