Tags : classification   diabetes disease diagnosis   smooth support vector machine  more

Tags : classification   diabetes disease diagnosis   smooth support vector machine   modified spline function  less

Reference : 1. http://www.medicalnewstoday.com/articles/44967.php 2. C.M. Huang, Y.J. Lee, D.K.J. Lin, S.Y. Hu    more

Reference : 1. http://www.medicalnewstoday.com/articles/44967.php 2. C.M. Huang, Y.J. Lee, D.K.J. Lin, S.Y. Huang. Model Selection for Support Vector Machines via Uniform Design. A Special issue on Machine Learning and Robust Data Mining of Computational Statistics and Data Analysis, vol. 52, 335-346, (2007). 3. Hsu, C.W., C.C. Chang and C.J. Lin, Practical guide to support vector classification. Department of Computer Science and Information Engineering National Taiwan University. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf, (2003). 4. Kahramanli, H and Allahverdi, N, Design of a hybrid system for the diabetes and heart diseases. Expert Systems with Applications, 35, 82-89, (2008). 5. Kayaer, K.and T. Yildirim, Medical diagnosis on Pima Indian diabetes using general regression neural networks. Proceedings of the International Conference on Artificial Neural Networks and Neural Information Processing, Springer, Istanbul-Turkey, June 26-29, pp: 181-184, (2003). 6. Kohavi, R., and Provost, F. Glossary of terms. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process, 30, 2-3, (1998). 7. Lee, Y.J. and O.L. Mangasarian, A smooth support vector machine. J. Comput. Optimiz. Appli., 20: 5-22, (2001). 8. Mangasarian, O.L., Generalized Support Vector Machines. In: Advances in large Margin Classifiers, Smola, A., P. Bartlett, B. Scholkopf and D. Schurrmans (Eds.). MIT Press, Cambridge, MA.,ISBN: 0-262-19448-1, pp: 35-146, (2000). 9. Newman, D.J., Hettich, S., Blake, C. L. S., & Merz, C. J., UCI repository of machine learning database, Irvine, CA: University of California, Dept. of Information and Computer Science. http://www.ics.uci.edu/~mlearn/~MLRepository.html, (1998). 10. Polat, K. and S. Gunes, An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease. Digital Signal Processing, 17, 702-710, (2007). 11. Polat, K., S. Gunes and A. Aslan, A Cascade Learning system for classification of diabetes disease: Generalized discriminant analysis and least square support vector machine. Expert System with Applications, 34, 214-22, (2008). 12. Temurtas, H. et al., A comparative study on diabetes disease using neural networks. Expert System with Applications, 36, 8610-8615, (2009). 13. Yuan, Y., W. Fan and D. Pu, Spline function smooth support vector machine for classification. J. Ind. Manage. Optimiz., 3: 529-542, (2007). 14. Vapnik, V., The Nature of Statistical Learning Theory. Second edition, Springer-Verlag, New York. pp. 138-141. ISBN: 0-387-98780-0, (1995).    less

Abstract : In recent years, the uses of intelligent methods in biomedical studies are growing gradually. In th    more

Abstract : In recent years, the uses of intelligent methods in biomedical studies are growing gradually. In this paper, a novel method for diabetes disease diagnosis using modified spline smooth support vector machine (MS-SSVM) is presented. To obtain optimal accuracy results, we used Uniform Design method for selection parameter. The performance of the method is evaluated using 10-fold cross validation accuracy, confusion matrix, sensitivity and specificity. The comparison with previous spline SSVM in diabetes disease diagnosis also was given. The obtained classification accuracy using 10-fold cross validation is 96.58%. The results of this study showed that the modified spline SSVM was effective to detect diabetes disease diagnosis and this is very promising result compared to the previously reported results- Slides    less