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dc.contributor.authorTurpin, Andrew
dc.contributor.authorFrank, Eibe
dc.contributor.authorHall, Mark A.
dc.contributor.authorWitten, Ian H.
dc.contributor.authorJohnson, Chris A.
dc.coverage.spatialConference held at Hong Kong, Chinaen_NZ
dc.date.accessioned2008-11-13T23:03:44Z
dc.date.available2008-11-13T23:03:44Z
dc.date.issued2001
dc.identifier.citationTurpin, A., Frank, E., Hall, M., Witten, I.H. & Johnson, C.A. (2001). Determining progression in glaucoma using visual fields. In Proceedings of Advances in Knowledge Discovery and Data Mining 5th Pacific-Asia Conference, PAKDD 2001 Hong Kong, China, April 16–18, 2001 (pp. 136-147), Berlin: Springer.en_US
dc.identifier.urihttps://hdl.handle.net/10289/1327
dc.description.abstractThe standardized visual field assessment, which measures visual function in 76 locations of the central visual area, is an important diagnostic tool in the treatment of the eye disease glaucoma. It helps determine whether the disease is stable or progressing towards blindness, with important implications for treatment. Automatic techniques to classify patients based on this assessment have had limited success, primarily due to the high variability of individual visual field measurements. The purpose of this paper is to describe the problem of visual field classification to the data mining community, and assess the success of data mining techniques on it. Preliminary results show that machine learning methods rival existing techniques for predicting whether glaucoma is progressing—though we have not yet been able to demonstrate improvements that are statistically significant. It is likely that further improvement is possible, and we encourage others to work on this important practical data mining problem.en_US
dc.language.isoen
dc.publisherSpringer Berlinen_US
dc.relation.urihttp://www.springerlink.com/content/hkduv7lgk4ggqeal/en_US
dc.sourcePAKDD 2001en_NZ
dc.subjectcomputer scienceen_US
dc.subjectMachine learning
dc.titleDetermining progression in glaucoma using visual fieldsen_US
dc.typeConference Contributionen_US
dc.identifier.doi10.1007/3-540-45357-1_17en_US
dc.relation.isPartOfProc 5th Pacific-Asia Conference on Knowledge Discovery and Data Miningen_NZ
pubs.begin-page136en_NZ
pubs.elements-id11600
pubs.end-page147en_NZ
pubs.finish-date2001en_NZ
pubs.start-date2001en_NZ
pubs.volumeLNCS 2035en_NZ


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