Bayesian network classifier for medical data analysis

Abstract

Bayesian networks encode causal relations between variables using probability and graph theory. They can be used both for prediction of an outcome and interpretation of predictions based on the encoded causal relations. In this paper we analyse a tree-like Bayesian network learning algorithm optimised for classification of data and we give solutions to the interpretation and analysis of predictions. The classification of logical - i.e. binary - data arises specifically in the field of medical diagnosis, where we have to predict the survival chance based on different types of medical observations or we must select the most relevant cause corresponding again to a given patient record. Surgery survival prediction was examined with the algorithm. Bypass surgery sur- vival chance must be computed for a given patient, having a data-set of 66 medical examinations for 313 patients. Copyright © 2006-2009 by CCC Publications.