Browse > Article
http://dx.doi.org/10.9717/kmms.2012.15.6.701

Pattern Classification of Retinitis Pigmentosa Data for Prediction of Prognosis  

Kim, Hyun-Mi (창원대학교 컴퓨터공학과)
Woo, Yong-Tae (창원대학교 컴퓨터공학과)
Jung, Sung-Hwan (창원대학교 컴퓨터공학과)
Publication Information
Journal of Korea Multimedia Society / v.15, no.6, 2012 , pp. 701-710 More about this Journal
Abstract
Retinitis Pigmentosa(RP) is a common hereditary disease. While they have been normally living, those who have this symptom feel frustration and pain by the damage of visual acuity. At the national level, the loss of the economic activity due to the reduction of economically active population will be also greater. There is an urgent need for the base study that can provide the clinical prognosis information of RP disease. In this study, we suggest that it is possible to predict prognosis through the pattern classification of RP data. Statistical processing results through statistical software like SPSS(Statistical Package for the Social Service) were mainly applied for the conventional study in data analysis. However, machine learning and automatic pattern classification was applied to this study. SVM(Support Vector Machine) and other various pattern classifiers were used for it. The proposed method confirmed the possibility of prognostic prediction based on the result of automatically classified RP data by SVM classifier.
Keywords
Prediction of Prognosis; Pattern Classification; SVM(Support Vector Machine); RP(Retinitis Piagmentosa); SPSS(Statistical Package for the Social Service);
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 유형곤, 유전성 망막질환, 서울대학교출판문화원, 서울, 2011.
2 Michael A. Sandberg, Robert J. Brockhurst, Alexander R. Gaudio, and Eliot L.Berson "The Association between Visual Acuity and Central Retinal Thickness Retinitis Pigmentosa," IOVS, Vol.46, No.9, pp. 3349-3354, 2005.
3 S Aizawa, Y Mitamura, T Baba, A Hagiwara, K Ogata, and S Yamamoto, "Correlation Between Visual Function and Photoreceptor Inner/Outer Segment Junction in Patients with Retinitis Pigmentosa," Eye, Vol.23, No. 2, pp. 304-308, 2009.   DOI
4 A Oishi, A Otani, M Sasahara, H Kojima, H Nakamura, M Kurimoto, and N Yoshimura "Photoreceptor Integrity and Visual Acuity in Cystoids Macular Oedema Associated with Retinitis Pigmentosa," Eye, Vol.23, No.6, pp. 1411-1416, 2009.   DOI
5 Michael A. Sandberg, Robert J. Brockhurst, Alexander R. Gaudio, and Eliot L. Berson "Visual Acuity is Related to Parafoveal Retinal Thickness in Patients with Retinitis Pigmentosa and Macular Cysts," IOVS, Vol. 49, No.10, pp. 4568-4572, 2008.
6 Hyewon Chung, Jong-Uk Hwang, June-Gone Kim, and Young Hee Yoon, "Optical Coherence Tomography in the Diagnosis and Monitoring of Cystoid Macular Edema in Patients with Retinitis Pigmentosa," Retina, Vol.26, No.8, pp. 922-927, 2006.   DOI
7 Ki-Kwang Lee and Chang Hee Han, "Medical Diagnosis Problem Solving Based on the Combination of Genetic Algorithms and Local Adaptive Operations," 지능정보연구, 제14권, 제2호, pp. 193-206, 2008.   과학기술학회마을
8 D. Nasien, S.S. Yuhaniz, and H. Haron, "Statistical Learning Theory and Support Vector Machines," Proceedings of the 2010 Second International Conference on Computer Research and Development, IEEE Computer Society, pp. 760-764, 2010.
9 강선경, 소인미, 김영운, 이상설, 정성태, "SVM과 LDA를 이용한 마커 검출 및 인식의 성능 향상," 멀티미디어학회논문지, 제10권, 제7호, pp. 923-933, 2007.   과학기술학회마을
10 Vladimir N. Vapnik, Statistical Learning Thoery, John Wiley & Sons, Hoboken, 1998.
11 Vladimir N. Vapnik, "An Overview of Statistical Learning thoery," IEEE Trans. on Neural Networks, Vol.10, No.5, pp. 988-999, 1999.   DOI   ScienceOn
12 Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification(2nd ed.) , Wiley Interscience, New York, 2001.
13 오일석, 패턴인식, 교보문고, 서울, 2008.
14 Chin-Wei Hsu,Chih-Chung Chang, and Chih- Jen Lin, A Practical Guide to Support Vector Classification, http://www.csie.ntu.edu.tw/∼cjlin/papers/guide/guide.pdf, 2010.
15 Chih-Wei Hsu and Chih-Jen Lin, "A Comparison of Methods for Multiclass Support Vector Machines," IEEE Trans. on Neural Networks, Vol.13, No.2, pp. 415-425, 2002.   DOI   ScienceOn
16 D. Meyer, F. Leisch, and K. Hornik, "The Support Vector Machine Under Test," Neurocom puting, Vol. 55, Issues. 1-2, pp. 169-186, 2003.   DOI
17 Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a Library for Support Vector Machines, http://www.csie.ntu.edu.tw/∼cjlin/libsvm, 2010.