A Spatio-Temporal Bayesian Network Classifier for Understanding Visual Field Deterioration

Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the ‘nasal step’, an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data

Bayesian networks to explain the effect of label information on product perception

Interdisciplinary approaches in food research require new methods in data analysis that are able to deal with complexity and facilitate the communication among model users. Four parallel full factorial within-subject designs were performed to examine the relative contribution to consumer product evaluation of intrinsic product properties and information given on packaging. Detailed experimental designs and results obtained from analyses of variance were published [1]. The data was analyzed again with the machine learning modelling technique Bayesian networks. The objective of the current paper is to explain basic features of this technique and its advantages over the standard statistical approach regarding handling of complexity and communication of results. With analysis of variance, visualization and interpretation of main effects and interactions effects becomes difficult in complex systems. The Bayesian network model offers the possibility to formally incorporate (domain) experts knowledge. By combining empirical data with the pre-defined network structure, new relationships can be learned, thus generating an update of current knowledge. Probabilistic inference in Bayesian networks allows instant and global use of the model; its graphical representation makes it easy to visualize and communicate the results. Making use of the most of data from one single experiment, as well as combining data of independent experiments makes Bayesian networks for analysing these and similarly complex and rich data set