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A Comparison of Different Prediction Models in the Progression of Ocular hypertension to Primary Open Angle Glaucoma

by M. I. Waly, Amr Sharawy, K. Wahba
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 3
Year of Publication: 2013
Authors: M. I. Waly, Amr Sharawy, K. Wahba
10.5120/ijais12-450871

M. I. Waly, Amr Sharawy, K. Wahba . A Comparison of Different Prediction Models in the Progression of Ocular hypertension to Primary Open Angle Glaucoma. International Journal of Applied Information Systems. 5, 3 ( February 2013), 30-42. DOI=10.5120/ijais12-450871

@article{ 10.5120/ijais12-450871,
author = { M. I. Waly, Amr Sharawy, K. Wahba },
title = { A Comparison of Different Prediction Models in the Progression of Ocular hypertension to Primary Open Angle Glaucoma },
journal = { International Journal of Applied Information Systems },
issue_date = { February 2013 },
volume = { 5 },
number = { 3 },
month = { February },
year = { 2013 },
issn = { 2249-0868 },
pages = { 30-42 },
numpages = {9},
url = { https://www.ijais.org/archives/volume5/number3/428-0871/ },
doi = { 10.5120/ijais12-450871 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T17:58:15.731708+05:30
%A M. I. Waly
%A Amr Sharawy
%A K. Wahba
%T A Comparison of Different Prediction Models in the Progression of Ocular hypertension to Primary Open Angle Glaucoma
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 5
%N 3
%P 30-42
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The issue of risk assessment in glaucoma has ?received increasing attention in the past few ?years. Predictive models are in order to ?estimate the risk that patients with ocular ?hypertension will develop to primary open ?angle glaucoma (POAG) if left untreated. ?These models are based on classification ?techniques on the risk factors. Classification is ?accomplished using conventional risk factors ?besides retinal nerve fiber layer (RNFL) ?thickness. It was found that RNFL is sensitive ?to glaucomatous damage by using different ?classification algorithms in order to reach to ?best prediction model. ? We have applied the Decision tree (DT), Fuzzy ?logic and Neural Network to the glaucoma ?problem. The performances of the various ?classifiers are compared by the area under the ?receiver operating characteristics curve ??(AUROC) and the accuracy. The decision tree ?classifier gives the best result with accuracy 8o% for the training dataset, ??68. 7% for testing data set with AUROC 0. 868.

References
  1. ? Quigley HA, Number of people with ?glaucoma worldwide, Br J ?Ophthalmol, ??1996;80(5):389–93??. ? ?
  2. ? Quigley HA, Broman AT. The number of ?people with glaucoma ?worldwide in 2010 and ??2020. Br J ?Ophthalmol 2006 Mar;90(3):262-7? ?
  3. ? Tielsch JM, Sommer A, Katz J, et al. , Racial ?variations in the prevalence of ?primary open-?angle glaucoma. The Baltimore Eye ?Survey, ??1991;266:369–74??. ? ?
  4. ? Mitchell P, Smith W, Attebo K, Healey PR, ?Prevalence of open angle ?glaucoma in ?Australia. The Blue Mountains Eye Study?,???Ophthalmology, ??1996;103:1661–9??. ? ?
  5. ? Dielemans I, VingerlingJR,Wolfs RCW, et ?al. , The prevalence of primary ?open-angle ?glaucoma in a population-based study in the ?Netherlands. The ?Rotterdam Study, ?Ophthalmology 1994;101:1851–5. ? ?
  6. Topouzis F, Wilson MR, Harris A, et al. , ?Prevalence of open angle ?glaucoma in Greece. ?The Thessaloniki Eye Study, Am J ?Ophthalmol?,????2007;144:511–19. ? ?
  7. G. E. Trope, Glaucoma: A Patient's Guide ?to the Disease (Univ. of ?Toronto Pr. , 2001)? ?
  8. J. J. Kanski, J. A. McAllister, Glaucoma: A ?Coulour Manual of Diagnosis ?and Treatment ??(Butterworths, London, Boston, Singapore, ?Sydney, ?Toronto, Wellington, 1989? ?
  9. ? Kanski JJ. CLINICAL ?OPHTHALMOLOGY. Oxford: Butterworth ?Heinemann, 2003. (Guideline Ref ID: ?KANSKI2003) page 102:135. ? ?
  10. ?Tielsch, J. M. , Katz, J. , et al. A population-?based evaluation of ?glaucoma screening: the ?Baltimore eye survey?. ?? Am. J. Epidemiol. , ??1991 ??134(10): 1102–1110. ? ?
  11. . Gordon, M. O. , Beiser, J. A. , et al. The ?ocular hypertension treatment ?study: baseline ?factors that predict the onset of primary open-?angle ?glaucomaArch. Ophthalmol. ,2002,714–??720; discussion 829–830. ? ?
  12. Miglior, S. , Pfeiffer, N. , et al. Predictive ?factors for open angle ?glaucoma among ?patients with ocular hypertension in the ?European ?glaucoma prevention study. ?Ophthalmology?,?? 2007 114(1): 3–9. ? ?
  13. ? Gordon, M. O. , Torri, V. , et al. ?Validated prediction model for ?the ?development of primary open-angle ?glaucoma in individuals with ?ocular ?hypertension. Ophthalmology?,??2007 ??,114(1): 10–19. ? ?
  14. ? Miglior, S. Risk factors for ?development of glaucoma. ?European ?Ophthalmology review , 2007, ??105(1): 4–8. ? ?
  15. ? Gordon, M. O. and Kass, M. A. The ?ocular hypertension treatment ?study: design ?and baseline description of the participants. ?Arch. ?Ophthalmol. , 1999, 117(5): 573–583. ? ?
  16. ? Friedman DS, Wilson MR, Liebmann ?JM, et a. An evidence-based ?assessment of risk ?factors for the progression of ocular ?hypertension and ?glaucoma. Am J Ophthalmol ??2004 Sep;138(3 Suppl):S19-31. ? ?
  17. ? Anderson DR, Drance SM, Schulzer ?M; Collaborative Normal-Tension ?Glaucoma ?Study Group. Factors that predict the benefit ?of lowering ?intraocular pressure in normal ?tension glaucoma. Am J Ophthalmol ??2003 ?Nov;136(5):820-9. ? ?
  18. ? Shiose Y. The aging effect on ?intraocular pressure in an apparently ?normal ?population. Arch Ophthalmol 1984 ?Jun;102(6):883-7. ? ?
  19. ? Detry-Morel M, Zeyen T, Kestelyn P, ?et al. Screening for glaucoma in a ?general ?population with the non-mydriatic fundus ?camera and the ?frequency doubling perimeter. ?Eur J Ophthalmol 2004 SepOct;14(5):387-??93. ? ?
  20. ? Ehlers, N. , Bramsen, T. , et al. ?Applanation tonometry and central ?corneal ?thickness. ActaOphthalmol. 1975,53(1): 34–??43. ? ?
  21. ? Whitacre, M. M. , Stein, R. A. , et al. The ?effect of corneal thickness on ?applanation ?tonometry. Am. J. Ophthalmol?. ??2004??,??115(5): ??592–596. ? ?
  22. ? Herndon, L. W. , Choudhri, S. A. , et al. ?Central corneal thickness in ?normal, ?glaucomatous, and ocular hypertensive eyes. ?Arch. Ophthalmol. ,??1997,115(9): 1137–1141. ? ?
  23. ? Copt, R. P. , Thomas, R. , et al. Corneal ?thickness in ocular hypertension, ?primary ?open-angle glaucoma, and normal tension ?glaucoma. Arch. ?Ophthalmol. , 1999,117(1): ??14–16. ? ?
  24. ? Doughty, M. J. and Zaman,M. L. ?Human corneal thickness and its ?impact on ?intraocular pressure measures: a review and ?meta-analysis ?approach. Surv. Ophthalmol. ??2000,44(5): 367–408. ? ?
  25. ? Brandt, J. D. The influence of corneal ?thickness on the diagnosis and ?management of ?glaucoma. J. Glaucoma, 2001, 10(5 (Suppl 1)): ?S65–S67. ? ?
  26. ? Sommer A. Retinal nerve fiber layer. ?Am J Ophthalmol. 1995;120:665–??667? ?
  27. ? Sommer A, Katz J, Quigley HA, et al. ?Clinically detectable nerve fiber ?atrophy ?precedes the onset of glaucomatous field loss. ?Arch Ophthalmol. ??1991;109:77–83? ?
  28. ? Sommer A, Miller NR, Pollack I, et?al. The nerve fiber layer in the ?diagnosis of ?glaucoma. Arch Ophthalmol. 1977;95:2149–??2156. ? ?
  29. Caprioli j. Discrimination between ?normal and glaucomatous eyes. ?Invest ?opthalmolvis sci. 1992;33;153-159? ?
  30. ? Mardin CY. And Peter A. Improving?glaucoma diagnosis by ?combination of ?perimetry and HRT measurements. J ?Glaucoma. ??2006;15;299-305. ? ?
  31. ? Shah NN. Combining structural and ?functional testing for detection ?of ?glaucoma. opthalmology. 2006;113;1593-??1602. ? ?
  32. ? ? R. T. Cox, The Algebra of Probable ?Inference. Baltimore, MD: Johns?Hopkins ?Univ. Press, 1961. ? ?
  33. ? N. Friedman, D. Geiger, and M. ?Goldszmidt, "Bayesian network ?classifiers," ?Machine Learning, , 1997, vol. 29, pp. 131–??163. ? ?
  34. ? T. Mitchell, Machine Learning. New ?York: McGraw-Hill, 1997? ?
  35. ? D. R. Cox and E. J. Snell, Analysis of ?Binary Data, 2nd ed. London, ?U. K. : Chapman ?and Hall, 1989. ? ?
  36. ? L. K. Saul and D. D. Lee, ??"Discriminative mixture modeling," ?in ?Advances in Neural Information Processing ?Systems 14. Cambridge, MA: ?MIT Press, 2002? ?
  37. ? ?L. Breiman, J. H. Friedman, R. A. ?Olshen, and C. J. Stone, ?Classification and ?Regression Trees. New York: Chapman and ?Hall, 1993. ? ?
  38. ? ?J. R. Quinlan, C4. 5: Programs for ?Machine Learning. San Mateo, CA: ?Morgan ?Kaufmann, 1993. ? ?
  39. ? ? C. M. Bishop, Neural Networks for ?Pattern Recognition. Oxford, ?U. K. : Clarendon, ??1995. ? ?
  40. ? ? B. D. Ripley, Pattern Recognition and ?Neural Netwroks. Cambridge, ?U. K. : Cambridge ?Univ. Press, 1996. ? ?
  41. ? ?V. N. Vapnik, The Nature of ?Statistical Learning Theory, 2nd ed. New ?York: ?Springer-Verlag, 2000. ? ?
  42. ? Quinlan, J. R. Learning efficient ?classification procedures and their ?application ?to chess endgames. In R. S. Michalski, J. G. ?Carbonell& T. M. ?Mitchell, (Eds. ), Machine ?learning: An artificial intelligence approach. ?Palo ?Alto: Tioga Publishing Company (1983a). ? ?
  43. ? Quinlan, J. R. Decision trees and multi-?valued attributes. In J. E. Hayes ?& D. Michie ??(Eds. ), Machine intelligence 11. Oxford ?University Press (in ?press) (1985b). ? ?
  44. ? T. Sharp. Implementing decision trees ?and forests on a gpu. In ECCV, ??2008. 2, 5? ?
  45. ? D. Marr, Vision ,W. H. Freeman and ?Company, San Francisco, ?California, 1982? ?
  46. ? N. Varachiu, A Fuzzy Shapes ?Characterization for Robotics, Lecture ?Notes ?in Computer Science, vol. 1625, ?Computational Intelligence - Theory ?and ?Applications, Editor: B. Reusch, Sprienger-?Verlag, Berlin, Heidelberg, ?New York, 1999, pp. ??253-258? ?
  47. ? M. Ulieru, Fuzzy logic in diagnosis: ?possibilistic network, invited ?chapter in Fuzzy ?Logic (J. Baldwin, Ed. ) John Wiley & Sons, ?ISBN ?o471962813, 1996 ? ?
  48. ? Fuzzy Control Manager, Version ??1. 5. 4, TransferTech GmbH, ?Braunschweig, ?Germany, 2001? ?
  49. ? Heckerling PS, Gerber BS, Tape TG, ?Wigton RS. Entering the black ?box of neural ?networks. Methods Inf Med 2003; 42 (3): 287-??296. ? ?
  50. ? Seuchter SA, Eisenacher M, Riesbeck ?M, Gaebel W, Köpcke W, other ?members of ?the A. N. I. Study Group. Methods for predictor ?analysis of ?repeated measurements: ?application to psychiatric data. Methods Inf?Med ??2004; 43 (2): 184-191. ? ?
  51. ? Rumelhart DE, Hinton GE, Williams ?RJ. Learning internal ?representations by error ?propagation. In: Rumelhart DE, McClelland ?JL, ?editors. Parallel distributed processing (Vol ??1). Cambridge, MA: MIT ?Press; 1986. pp 318-??362. ? ?
  52. ? Moller MF: A Scaled conjugate ?gradient algorithm for fast supervised ?learning. ?Neural Networks,1993,6:525-533? ?
  53. ? Haykin SS: neural networks: a ?comprehensive foundation. Upper ?saddle ?River,1999. ? ?
  54. ? Stehman, Stephen V. (1997). ??"Selecting and interpreting measures ?of ?thematic classification accuracy". Remote ?Sensing of Environment 62 (1): ??77–89. ?doi:10. 1016/S0034-4257(97)00083-7?
Index Terms

Computer Science
Information Sciences

Keywords

Glaucoma primary open angle glaucoma retinal fiber layer generative and discriminative classifiers

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