Classifying Glaucomatous Progression using Decision Trees

M. Lazarescu and A. Turpin (Australia)


: Data Mining, Machine Learning, Knowledge Representation, Pattern Recognition.


This paper presents a method to determine glaucomatous progression using features derived from S.A.P. (Standard Automated Periphery) data. The method exploits the con sistency in eyesight degradation that occurs in patients that have progressive glaucoma. The main advantage offered by our approach is that it is accurate and it uses a small and simple feature set. We describe the set of features used and we present our classification results obtained using C4.5.

Important Links:

Go Back