Exploring early glaucoma and the visual field test: classification and clustering using Bayesian networks.

Abstract

Bayesian networks (BNs) are probabilistic models used for classification and clustering in several fields. Their ability to deal with unobserved variables and to integrate data and expert knowledge make them an appropriate technique for modeling eye functionality measurements in glaucoma. In this study, a set of BNs is used to simultaneously perform classification of early glaucoma and cluster data into different stages of disease. A novel learning algorithm that combines clustering and quasi-greedy search is also proposed. The classification performances of the models are evaluated on an independent dataset, while the clusters are compared to K-means, previous publications, and direct knowledge. The use of clustering and structure learning enabled the exploration of the visual field patterns of the disease while obtaining good results both on pre- (50% sensitivity at 90% specificity) and post- (85% sensitivity at 90% specificity) diagnosis data. Clusters obtained were insightful and in conformity with consolidated knowledge in the field.