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.
The following grants supported our work: KBN 8T11E02917, W/II/1/00, AFOSR F49620-00-1-0112, NSF IRI-9624629, NATO PST.CLG.976167.
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Oniésko, A., Lucas, P., Druzdzel, M.J. (2001). Comparison of Rule-Based and Bayesian Network Approaches in Medical Diagnostic Systems. In: Quaglini, S., Barahona, P., Andreassen, S. (eds) Artificial Intelligence in Medicine. AIME 2001. Lecture Notes in Computer Science(), vol 2101. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48229-6_40
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DOI: https://doi.org/10.1007/3-540-48229-6_40
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