Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10478
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dc.contributor.authorSacchi, L-
dc.contributor.authorTucker, A-
dc.contributor.authorCounsell, S-
dc.contributor.authorGarway-Heath, D-
dc.contributor.authorSwift, S-
dc.date.accessioned2015-03-23T15:49:50Z-
dc.date.available2014-02-
dc.date.available2015-03-23T15:49:50Z-
dc.date.issued2014-
dc.identifier.citationArtificial Intelligence in Medicine, 2014, 60, pp. 103 - 112en_US
dc.identifier.issnS0933365713001620-
dc.identifier.issn0933-3657-
dc.identifier.issn1873-2860-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/10478-
dc.descriptionThis article has been made available through the Brunel Open Access Publishing Fund.-
dc.description.abstractObjective: 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.en_US
dc.format.extent103 - 112-
dc.format.extent103 - 112-
dc.languageeng-
dc.language.isoenen_US
dc.subjectGlaucoma severity predictionen_US
dc.subjectPredictive modellingen_US
dc.subjectReliability indicatorsen_US
dc.subjectVisual field testingen_US
dc.titleImproving predictive models of glaucoma severity by incorporating quality indicatorsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.artmed.2013.12.002-
dc.relation.isPartOfArtificial Intelligence in Medicine-
dc.relation.isPartOfArtificial Intelligence in Medicine-
pubs.issue2-
pubs.issue2-
pubs.volume60-
pubs.volume60-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Computer Science-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Computer Science/Computer Science-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies/Synthetic Biology-
pubs.organisational-data/Brunel/Specialist Centres-
pubs.organisational-data/Brunel/Specialist Centres/IfE-
pubs.organisational-data/Brunel/University Research Centres and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/Brunel Business School - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/Brunel Business School - URCs and Groups/Centre for Research into Entrepreneurship, International Business and Innovation in Emerging Markets-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute for Ageing Studies-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute of Cancer Genetics and Pharmacogenomics-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Multidisclipary Assessment of Technology Centre for Healthcare (MATCH)-
Appears in Collections:Brunel OA Publishing Fund
Dept of Computer Science Research Papers

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