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An application of machine learning techniques for the classification of glaucomatous progression

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conference contribution
posted on 2002-01-01, 00:00 authored by M Lazarescu, A Turpin, Svetha VenkateshSvetha Venkatesh
This 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

Pagination

243 - 251

Location

Ontario, Canada

Open access

  • Yes

Start date

2002-08-06

End date

2002-08-09

ISBN-10

3540440119

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2002, Springer-Verlag Berlin Heidelberg

Editor/Contributor(s)

T Caelli, A Amin, R Duin, M Kamel, D de Ridder

Title of proceedings

Structural, syntactic, and statistical pattern recognition : joint IAPR International Workshops SSPR 2002 and SPR 2002 proceedings

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