An Application of Machine Learning Techniques for the Classification of Glaucomatous Progression

  • Mihai Lazarescu
  • Andrew Turpin
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


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.


Machine Learn Technique Time Instance Incremental Learning Concept Drift Stable Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Mihai Lazarescu
    • 1
  • Andrew Turpin
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Department of Computer ScienceCurtin UniversityPerthAustralia

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