Determining Progression in Glaucoma Using Visual Fields

  • Andrew Turpin
  • Eibe Frank
  • Mark Hall
  • Ian H. Witten
  • Chris A. Johnson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2035)


The 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.


Support Vector Machine Glaucoma Patient Linear Support Vector Machine Data Mining Approach Glaucoma Progression 
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 2001

Authors and Affiliations

  • Andrew Turpin
    • 1
  • Eibe Frank
    • 2
  • Mark Hall
    • 2
  • Ian H. Witten
    • 2
  • Chris A. Johnson
    • 1
  1. 1.Discoveries in Sight, Devers Eye InstitutePortlandUSA
  2. 2.Department of Computer ScienceUniversity of WaikatoHamiltonNew Zealand

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