Abstract:
This study subjective to deal with probabilistic inference of lung cancer diagnosis involving features that are not directly related, and for which the conditional probability cannot be readily computed using a simple application of the Bayes' theorem that illustrates a simple Bayesian Network example for exact probabilistic inference using Pearl's message-passing algorithm to model the diagnostic of lung cancer.
The model of diagnosis examined over 200 patients and the results were been satisfied.
