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An application of machine learning techniques for the classification of glaucomatous progression
conference contribution
posted on 2002-01-01, 00:00 authored by M Lazarescu, A Turpin, Svetha VenkateshSvetha VenkateshThis paper presents an application of machine learning to the problem of classifying patients with glaucoma into one of two classes:stable and progressive glaucoma. The novelty of the work is the use of new features for the data analysis combined with machine learning techniques to classify the medical data. The paper describes the new features and the results of using decision trees to separate stable and progressive cases. Furthermore, we show the results of using an incremental learning algorithm for tracking stable and progressive cases over time. In both cases we used a dataset of progressive and stable glaucoma patients obtained from a glaucoma clinic.
History
Event
International Workshop on Syntactic and Structural Pattern Recognition (9th : 2002 : Ontario, Canada)Pagination
243 - 251Publisher
SpringerLocation
Ontario, CanadaPlace of publication
[Berlin, Germany]Publisher DOI
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Start date
2002-08-06End date
2002-08-09ISBN-10
3540440119Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2002, Springer-Verlag Berlin HeidelbergEditor/Contributor(s)
T Caelli, A Amin, R Duin, M Kamel, D de RidderTitle of proceedings
Structural, syntactic, and statistical pattern recognition : joint IAPR International Workshops SSPR 2002 and SPR 2002 proceedingsUsage metrics
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