Using Bayesian Network Inference to Modeling Lung Cancer Diagnosis

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.