An Improved Prediction Approach for Progression of Ocular Hypertension to Primary Open Angle Glaucoma

  • Mohamed Abd Elfattah
  • M. I. Waly
  • Mohamed A. Abu Elsoud
  • Aboul Ella Hassanien
  • Mohamed F. Tolba
  • Jan Platos
  • Gerald Schaefer
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)


In this paper, we present an improved prediction model for progression of ocular hypertension to primary open angle glaucoma using a random forest classification approach. Our model comprises two phases: risk factor calculation and prediction. We start by calculating the risk factors associated with the outcome, followed by a prediction phase that utilises a random forest approach for classification into one of four obtained classes, namely low, mid, high, and moderate. Experimental results show that the employed random forest classifier provides better prediction accuracy compared to other machine learning techniques including Bayes net, multi-layer perceptron, radial basis function and naive Bayes tree classifiers.


Glaucoma primary open angle glaucoma retinal fiber layer machine learning pattern classification random forest classification 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mohamed Abd Elfattah
    • 1
    • 7
  • M. I. Waly
    • 2
  • Mohamed A. Abu Elsoud
    • 1
  • Aboul Ella Hassanien
    • 3
    • 7
  • Mohamed F. Tolba
    • 4
  • Jan Platos
    • 5
  • Gerald Schaefer
    • 6
  1. 1.Faculty of Computers and Information SystemMansoura UniversityMansouraEgypt
  2. 2.Biomedical Engineering Dept.El-Shrouk AcademyCairoEgypt
  3. 3.Faculty of Computers and InformationCairo UniversityCairoEgypt
  4. 4.Faculty of Computers and InformationAin Shams UniversityCairoEgypt
  5. 5.Faculty of Electrical Engineering & Computer ScienceVSB-TUOstravaCzech Republic
  6. 6.Department of Computer ScienceLoughborough UniversityLoughboroughU.K.
  7. 7.Scientific Research Group in Egypt (SRGE)CairoEgypt

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