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Bayesian networks [2] represent a promising technique for clinical decision support and provide powerful capabilities for representing uncertain knowledge, including a flexible representation of probability distributions that allows one to specify dependence and independence of variables in a natural way through the network topology. Because dependencies are expressed qualitatively as links between nodes, one can structure the domain knowledge qualitatively before any numeric probabilities need to be assigned.

Building a Bayesian network model of heart disease