Bayesian Network Based Netica for Respiratory Diseases

Abstract

The disease of the influenza, whose early symptoms are similar to other diseases, it is very difficult to diagnose a patients. Such as viral colds, bacterial colds and early symptoms of pneumonia. Based on the structural characteristics of Bayesian network in statistical probability, we do a diagnostic analysis of human respiratory diseases, so that doctors can provide basis for diagnosis of patients' diseases. General principle is the use of Bayesian network based on probabilistic reasoning directed acyclic graph model, the complex relationship between variables in the specific problems are expressed in a network structure, and through the network model to reflect the dependencies between the variables in the field of applied research, expression and reasoning on uncertainty knowledge. This is an example of applying the Bias network to the diagnosis of disease.