Comparison of rule-based and Bayesian network approaches in medical diagnostic systems

Authors:

Agnieszka Onisko
Bialystok University of Technology
Institute of Computer Science
Bialystok, 15-351, Poland
e-mail: aonisko@ii.pb.bialystok.pl

Peter Lucas
Department of Computing Science
University of Aberdeen
Aberdeen AB24 3UE
Scotland, UK
e-mail: plucas@csd.abdn.ac.uk

Marek J. Druzdzel
Decision Systems Laboratory
School of Information Sciences
and Intelligent Systems Program
University of Pittsburgh
e-mail: marek@sis.pitt.edu

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.