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Comparison of Rule-Based and Bayesian Network Approaches in Medical Diagnostic Systems

  • Agnieszka Oniésko
  • Peter Lucas
  • Marek J. Druzdzel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

Almost two decades after the introduction of probabilistic expert systems, their theoretical status, practical use, and experiences are matching those of rule-based expert systems. Since both types of systems are in wide use, it is more than ever important to understand their advantages and drawbacks. We describe a study in which we compare rule-based systems to systems based on Bayesian networks. We present two expert systems for diagnosis of liver disorders that served as the inspiration and vehicle of our study and discuss problems related to knowledge engineering using the two approaches. We finally present the results of a simple experiment comparing the diagnostic performance of each of the systems on a subset of their domain.

Keywords

Expert System Bayesian Network Joint Probability Distribution Causal Strength Bayesian Network Approach 
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 2001

Authors and Affiliations

  • Agnieszka Oniésko
    • 1
  • Peter Lucas
    • 2
  • Marek J. Druzdzel
    • 3
  1. 1.Institute of Computer Science, Bia lystokBia lystok University of TechnologyPoland
  2. 2.Department of Computing ScienceUniversity of AberdeenAberdeenUK
  3. 3.Decision Systems Laboratory, School of Information Sciences, Intelligent Systems Program, and Center for Biomedical InformaticsUniversity of PittsburghPittsburghUSA

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