Building a Bayesian network model of heart disease

Authors:
Jayanta K. Ghosh;Marco Valtorta
Affiliations:
University of South Carolina, Columbia, South Carolina;University of South Carolina, Columbia, South Carolina
Venue:
ACM-SE 38 Proceedings of the 38th annual on Southeast regional conference
Year:
2000

Citing 2
Cited 0

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Abstract

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.