Utilization of Discretization method on the diagnosis of optic nerve disease

Computer Methods and Programs in Biomedicine

Volumn 91, Issue 3, 2008, Pages 255-264

  Polat, Kemal ^a   Kara, Sadik ^b   Güven, Ayşegül ^c   Güneş, Salih ^a  

^a SELCUK UNIVERSITY   (Turkey)

^b FATIH UNIVERSITY   (Turkey)

^c ERCIYES UNIVERSITY   (Turkey)

Author keywords

Artificial neural network; C4.5 decision tree classifier; Discretization method; Hybrid systems; Least square support vector machine; Optic nerve disease; VEP signals

Indexed keywords

ALGORITHMS; ARCHITECTURAL DESIGN; BACKPROPAGATION; CLASSIFIERS; COMPUTER NETWORKS; CURVE FITTING; DATA PROCESSING; DECISION MAKING; DECISION THEORY; DECISION TREES; EVOLUTIONARY ALGORITHMS; EYE PROTECTION; HEALTH; LEARNING SYSTEMS; LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; METROPOLITAN AREA NETWORKS; NETWORK PROTOCOLS; NEURAL NETWORKS; PAPER; SUPPORT VECTOR MACHINES; TELECOMMUNICATION SYSTEMS; VOLUME MEASUREMENT;

ARTIFICIAL NEURAL NETWORK (ANNS); CLASSIFICATION ACCURACIES; CLASSIFICATION ALGORITHMS; CLASSIFIER ALGORITHMS; DATA PRE-PROCESSING; DECISION TREE CLASSIFIERS; DIAGNOSIS SYSTEMS; DISCRETE VALUES; DISCRETIZATION; DISCRETIZATION METHODS; ELSEVIER (CO); HYBRID DIAGNOSTIC; HYBRID METHODS; IN ORDER; INTELLIGENT ASSISTANCES; IRELAND; LEAST SQUARE SUPPORT VECTOR MACHINE (LSSVM); OPTIC NERVES; TWO PHASES; VISUAL EVOKED POTENTIAL (VEP);

CLASSIFICATION (OF INFORMATION);

ADULT; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DECISION TREE; DIAGNOSTIC ACCURACY; DIAGNOSTIC PROCEDURE; DICRETIZATION METHOD; DISEASE CLASSIFICATION; EVOKED VISUAL RESPONSE; FEMALE; HUMAN; INFORMATION PROCESSING; MAJOR CLINICAL STUDY; MALE; OPTIC NERVE DISEASE; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE;

ADULT; AGED; ALGORITHMS; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROENCEPHALOGRAPHY; EVOKED POTENTIALS, VISUAL; FEMALE; HUMANS; MALE; MIDDLE AGED; OPTIC NERVE DISEASES; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; VISUAL CORTEX;

EID: 46249105494     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2008.04.009     Document Type: Article

References (50)

1. Heckenlively J.R., and Arden G.B. Principles and Practice of Clinical Electrophysiological of Vision (1991), Mosby Year Book, United States of America
2. Tasman W. Duane's Foundations of Clinical Ophthalmology (1992), Lippincott Williams and Wilkins, United States of America
3. Stoll W., Buse H., and Kroll P. Decompression of the orbit and the optic nerve in different diseases. J. Cranio-Maxillofac. Surg. 16 (1988) 308-311
4. Ruther K., Ehlich P., Philipp A., Eckstein A., and Zrenner E. Prognostic value of the pattern electroretinogram in cases of tumors affecting the optic pathway. Graefe's Arch. Clin. Exp. Ophthalmol. 236 (1998) 259-263
5. Chamlin M., Davidoff L., and Feiring E. Ophthalmologic changes produced by pituitary tumors. Am. J. Ophthalmol. 40 (1955) 353-359
6. Vernon Odom J., et al. Visual evoked potentials standard. Documenta Ophthalmologica (2004), Kluwer Academic Publishers (Printed in The Netherlands)
7. Maffei L., and Fiorentini A. Electroretinographic responses to alternating gratings before and after sectioning of the optic nerve. Science (1981) 211-953
8. Atilla H., et al. Pattern electroretinography and visual evoked potentials in optic nerve diseases. J. Clin. Neurosci. 13 (2006) 55-59
9. Jancic-Stefanovic J., Kostic V., and Carelli V. Visual evoked potentials (VEP) and brainstem evoked potentials (BAEP) as diagnostic procedures in Leber's hereditary optic neuropathy. Clin. Neurophysiol. 118 4 (2007) 119
10. William P., Honan, Heron J.R., Foster D.H., Edgar G.K., and Scase M.O. Abnormalities of visual function in hereditary motor and sensory neuropathy. J. Neurol. Sci. 114 2 (1993) 188-192
11. Chang H.T., and Kaada B. An analysis of primary response of visual cortex to optic nerve stimulation in cats. J. Neurophysiol. 13 4 (1950) 304-318
12. Tsuneishi S., and Casaer P. Stepwise decrease in VEP latencies and the process of myelination in the human visual pathway. Brain Dev. 19 8 (1997) 547-551
13. Sokol S., and Moskowitz A. Effect of retinal blur on the peak latency of the pattern evoked potential. Vis. Res. 21 8 (1981) 1279-1286
14. Sivakumar R., and Ravindran G. Automatic discrimination of abnormal subjects using the visual evoked potential spectral components. J. Biomed. Biotechnol. 1 (2004) 5-9
15. Sivakumar R., Ravindran G., Muthayya M., Lakshminarayanan S., and Velmurughendran C.U. Diabetic retinopathy analysis. J. Biomed. Biotechnol. 1 (2005) 20-27
16. Rutecka A., et al. The study of PSVEP with dysthyroid optic neuropathy. 36th Symposium International Society for Clinical Electrophysiology of Vision. Hradce Kralove, Czech Republic (1998) (Abstracts)
17. Mghari A., Himmi M.M., Amaloud A., and Regragui F. Visual evoked potentials discrimination based on adaptive zero-tracking neural network. Comput. Biol. Med. 36 4 (2006) 408-418
18. McGregor A.M. Diagnosis of pituitary disease. Medicine 33 11 (2005) 2-7
19. Trick G.L., and Nesher R. The pattern electroretinogram in retinal and optic nerve disease. A quantitative comparison of the pattern of visual dysfunction. Doc. Ophthalmol. 77 (1991) 225-235
20. Holder G.E. Delayed pattern VEP: retinal or optic nerve disease?. Electroencephalogr. Clin. Neurophysiol. 69 (1988) 72-73
21. Holder G.E. The significance of abnormal pattern electroretinography in anterior visual pathway dysfunction. Br. J. Ophthalmol. 71 (1987) 166-171
22. Kara S., Güven A., and Öner A.O. Utilization of artificial neural networks in the diagnosis of optic nerve diseases. Comput. Biol. Med. 36 4 (2006) 428-437
23. Kara S., and Güven A. Training a learning vector quantization network using the pattern electroretinography signals. Comput. Biol. Med. 37 1 (2007) 77-82
24. Güven A., Polat K., Kara S., and Güneş S. The effect of generalized discriminate analysis (GDA) to the classification of optic nerve disease from VEP signals. Comput. Biol. Med. 38 1 (2008) 62-68
25. Kara S., and Güven A. Neural network based diagnosing for optic nerve disease from visual evoked potential. J. Med. Syst. 31 5 (2007) 391-396
26. Dougherty J., Kohavi R., and Sahami M. Supervised and un-supervised discrimination of continuous Features. In: Prieditis A., and Russell S. (Eds). Machine Learning: Proceedings of the 12th International Conference. Morgan Kauffman, San Francisco, CA (1995)
27. Friedman N., and Goldszmidt M. Discretization of con-tinuous attributes while learning Bayesian networks. In: Saitta L. (Ed). Proceedings of the 13th International Conference on Machine Learning. Morgan Kauffman, San Francisco, CA (1996) 157-165
28. Kauffman S.A. Metabolic stability and epigenesist in ran-domly constructed genetic nets. J. Theor. Biol. 22 (1969) 437-467
29. Albert R., and Othmer H. The topology of the regulatory interactions predict the expression pattern of the segment polar-ity genes in Drosophila melanogaster. J. Theor. Biol. 223 (2003) 1-18
30. Laubenbacher R., and Stigler B. A computational algebra approach to the reverse engineering of gene regulatory net-works. J. Theor. Biol. 229 (2004) 523-537
31. Thieffry D., and Thomas R. Qualitative analysis of gene networks. Proceedings of Pacific Symposium on Biocomputing. World Scientific, Singapore (1998) 77-88
32. Dimitrova E.S., John J., McGee J.J., and Laubenbacher R.C. Discretization of time series data. Quant. Biol. (2005) 1-8. http://polymath.vbi.vt.edu/discretization/DimitrovaMcGeeLaubenbacher.pdf Web site:
33. Mitchell M.T. Machine Learning (1997), McGraw-Hill, Singapore
34. Quinlan J.R. Induction of C4.5 Decision trees. Mach. Learn. 1 (1986) 81-106
35. Liu B., Hsu W., and Ma Y. Integrating classification and association rule mining. Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98, Plenary Presentation). New York, USA (1998)
36. http://www.doc.ic.ac.uk/∼nd/surprise_96/journal/vol4/cs11/report.html (last accessed: 22 March, 2008).
37. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer, New York
38. DTREG, http://www.dtreg.com/svm.htm (last accessed: 22 March, 2008).
39. Suykens J.A.K., and Vandewalle J. Least squares support vector machine classifiers. Neural Process. Lett. 9 3 (1999) 293-300
40. Tsujinishi D., and Abe S. Fuzzy least squares support vector machines for multi-class problems. Neural Netw. 16 56 (2003) 85-792
41. Jeff Schneider's Home Page, http://www.cs.cmu.edu/∼schneide/tut5/node42.html (last accessed: 22 March, 2008).
42. Kara S. Classification of mitral stenosis from Doppler signals using short time Fourier transform and artificial neural networks. Expert Syst. Appl. 33 2 (2007) 468-475
43. Zhang Y., Guo Z., Wang W., He S., Lee T., and Loew M. A comparison of the wavelet and short-time Fourier transforms for Doppler spectral analysis. Med. Eng. Phys. 25 7 (2003) 547-557
44. Rumelhart D.E., Hilton G.E., and Williams R.J. Learning representations by back-propagating errors. Nature 323 (1986) 533-536
45. Beale R., and Jackson T. Neural Computing: An Introduction (1990), Institute of Physics Publishing, Bristol, UK
46. Guler I., and Ubeyli E.D. Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network. Comput. Biol. Med. 33 4 (2003) 333-343
47. Haykin S. Neural Networks: A Comprehensive Foundation (1994), Macmillan College Publishing Company, New York
48. Turkoglu I., Arslan A., and Ilkay E. An expert system for diagnosis of the heart valve diseases. Expert Syst. Appl. 23 3 (2002) 229-236
49. Wright A., and Gough N.A.J. Artificial neural network analysis of common femoral artery Doppler shift signals: classification of proximal disease. Ultrasound Med. Biol. 24 5 (1999) 735-743
50. Johnson S.C. Hierarchical clustering schemes. Psychometrika 32 (1967) 241-254