Bayesian Classification and Forecasting of Visual Field Deterioration Allan Tucker, Xiaohui Liu; Brunel University David Garway-Heath; Moorfield’s Eye Hospital Moorfields Eye Hospital NHS Trust

Bayesian Networks Models the joint distribution of a domain Consists of a DAG and a set of conditional distributions P(X) = P(x 1 | x 2, x 3, … x n ) Combine expert knowledge with data

Architectures

Resulting Models - Quality

Classification and Prediction Overall specificity is better than sensitivity DBN appears best overall (temporal relationships) Successful at predicting conversion

Most Influential VF Points Proportion of DBN links generated during CV associated with predicting conversion Demonstrates classic ‘Nasal Step’ Deterioration in the nasal region of the VF indicate early signs of glaucoma

Summary Bayesian networks able to: Combine expert knowledge and data Make underlying model explicit Preliminary work carried out Learning different architectures for forecasting and classification

Future Work Comparisons to other classifiers Extending the datasets Intra-ocular pressure Demographic information Retina image data Continuous nodes (e.g. Gaussian, NN) Expert knowledge Anatomical Conversion Criteria