Genetic algorithm-evolved bayesian network classifier for medical applications

Abstract

This paper presents the development of a Bayesian Network (BN) classifier for a medical application. Patient age classification is based on statistical features extracted from electrocardiogram (ECG) signals. The computed ECG features are converted to a discrete form to lower the dimensionality of the signal and to allow for conditional probabilities to be calculated for the BN. Two methods of network discovery from data were developed and compared: a greedy hill-climb search and a search method based on evolutionary computing. The performance comparison of these two methods for network structure discovery shows a large increase in classification accuracy with the GA-evolved BN as measured by the area under the curve of the Receiver Operating Characteristic curve.