Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorTucker, A-
dc.contributor.authorCeccon, Stefano-
dc.descriptionThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.en_US
dc.description.abstractGlaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in obtaining sufficient measurements. This research aims to assess and extend Bayesian Network (BN) models to investigate the nature of the disease and its progression, as well as improve early diagnosis performance. The exibility of BNs and their ability to integrate with clinician expertise make them a suitable tool to effectively exploit the available data. After presenting the problem, a series of BN models for cross-sectional data classification and integration are assessed; novel techniques are then proposed for classification and modelling of glaucoma progression. The results are validated against literature, direct expert knowledge and other Artificial Intelligence techniques, indicating that BNs and their proposed extensions improve glaucoma diagnosis performance and enable new insights into the disease process.en_US
dc.publisherBrunel University, School of Information Systems, Computing and Mathematics-
dc.relation.ispartofSchool of Information Systems, Computing and Mathematics-
dc.subjectData analysisen_US
dc.subjectData miningen_US
dc.subjectMachine learningen_US
dc.subjectState space modelsen_US
dc.subjectArtifical intelligenceen_US
dc.titleExtending Bayesian network models for mining and classification of glaucomaen_US
Appears in Collections:Computer Science
Dept of Computer Science Theses

Files in This Item:
File Description SizeFormat 
FulltextThesis.pdf5.96 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.