Improving predictive models of glaucoma severity by incorporating quality indicators

Abstract

Objective: In this paper we present an evaluation of the role of reliability indicators in glaucoma severity prediction. In particular, we investigate whether it is possible to extract useful information from tests that would be normally discarded because they are considered unreliable. Methods: We set up a predictive modelling framework to predict glaucoma severity from visual field (VF) tests sensitivities in different reliability scenarios. Three quality indicators were considered in this study: false positives rate, false negatives rate and fixation losses. Glaucoma severity was evaluated by considering a 3-levels version of the Advanced Glaucoma Intervention Study scoring metric. A bootstrapping and class balancing technique was designed to overcome problems related to small sample size and unbalanced classes. As a classification model we selected Naïve Bayes. We also evaluated Bayesian networks to understand the relationships between the different anatomical sectors on the VF map. Results: The methods were tested on a data set of 28,778 VF tests collected at Moorfields Eye Hospital between 1986 and 2010. Applying Friedman test followed by the post hoc Tukey's honestly significant difference test, we observed that the classifiers trained on any kind of test, regardless of its reliability, showed comparable performance with respect to the classifier trained only considering totally reliable tests (p-value. >. 0.01). Moreover, we showed that different quality indicators gave different effects on prediction results. Training classifiers using tests that exceeded the fixation losses threshold did not have a deteriorating impact on classification results (p-value. >. 0.01). On the contrary, using only tests that fail to comply with the constraint on false negatives significantly decreased the accuracy of the results (p-value. <. 0.01). Meaningful patterns related to glaucoma evolution were also extracted. Conclusions: Results showed that classification modelling is not negatively affected by the inclusion of less reliable tests in the training process. This means that less reliable tests do not subtract useful information from a model trained using only completely reliable data. Future work will be devoted to exploring new quantitative thresholds to ensure high quality testing and low re-test rates. This could assist doctors in tuning patient follow-up and therapeutic plans, possibly slowing down disease progression. © 2013 Elsevier B.V.