DETERMINING THE CONDITIONAL PROBABILITIES IN BAYESIAN NETWORKS

Authors : Hülya OLMUŞ, S. Oral ERBAŞ

Pages : 69-76

View : 15 | Download : 6

Publication Date : 2004-01-01

Article Type : Research Paper

Abstract :Bayesian networks are used to illustrate how the probability of hav- ing a disease can be updated given the results from clinical tests. The problem of diagnosis, that is of determining whether a certain disease is present, $D$, or absent, $D`$, based on the result of a medical test, is discussed. Using statistical methods for medical diagnosis, informa- tion about the disease and symptoms are collected and the databases are used to diagnose new patients. How can we evaluate the diagnos- tic probability represented by Prinsert ignore into journalissuearticles values($D$\ evidence);, where evidence is the result of a clinical test or tests on a new patient? The object of this ar- ticle is to answer this question. Using the HUGIN software, diagnostic probabilities are analyzed using the Bayesian approach.
Keywords : Bayesian networks, Medical diagnosis, Conditional probability