Urinary System Diseases Diagnosis Using Machine Learning Techniques
Автор: Seyyid Ahmed Medjahed, Tamazouzt Ait Saadi, Abdelkader Benyettou
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 5 vol.7, 2015 года.
Бесплатный доступ
The urinary system is the organ system responsible for the production, storage and elimination of urine. This system includes kidneys, bladder, ureters and urethra. It represents the major system which filters the blood and any imbalance of this organ can increases the rate of being infected with diseases. The aim of this paper is to evaluate the performance of different variants of Support Vector Machines and k-Nearest Neighbor with different distances and try to achieve a satisfactory rate of diagnosis (infected or non-infected urinary system). We consider both diseases that affect the urinary system: inflammation of urinary bladder and nephritis of renal pelvis origin. Our experimentation will be conducted on the database “Acute Inflammations Data Set” obtained from UCI Machine Learning Repository. We use the following measures to evaluate the results: classification accuracy rate, classification time, sensitivity, specificity, positive and negative predictive values.
Urinary System, Diagnosis, Support Vector Machine, k-Nearest Neighbor, Distance
Короткий адрес: https://sciup.org/15010709
IDR: 15010709
Список литературы Urinary System Diseases Diagnosis Using Machine Learning Techniques
- S. Shah and A. Kusiak, “Cancer gene search with data-mining and genetic algorithms,” Computers in Biology and Medicine, vol. 37, 2002.
- J. Czerniak and H. Zarzycki, “Application of rough sets in the presumptive diagnosis of urinary system diseases,” Artifical Inteligence and Security in Computing Systems, ACS'2002 9th International Conference Proceedings, Kluwer Academic Publishers, 2003, pp. 41-51.
- Q. K. Al-Shayea, “Artificial neural networks in medical diagnosis,” IJCSI International Journal of Computer Science, vol. 8, no. 2, Mar 2011.
- Q. K. Al-Shayea and I. S. H. Bahia, “Urinary system diseases diagnosis using artificial neural networks,” IJCSNS International Journal of Computer Science and Network Security, vol. 10, no. 7, Jul 2010.
- S. Ghumbre, C. Patil and A. Ghatol, “Heart Disease Diagnosis using Support Vector Machine,” International Conference on Computer Science and Information Technology (ICCSIT'2011), Pattaya, pp. 84-88, 2011.
- K. Leung, Y. Ng, K. Lee, L. Chan, K. Tsui, T. Mok, C. Tse and J. Sung, “Data Mining on DNA Sequences of Hepatitis B Virus by Nonlinear Integrals,” Proceedings Taiwan-Japan Symposium on Fuzzy Systems & Innovational Computing, 3rd meeting, Japan, pp.1-10, 2006.
- L. Ozyilmaz, T. Yildirim, “Artificial neural networks for diagnosis of hepatitis disease,” Proceedings of the International Joint Conference on Neural Networks, Vol. 1, pp. 586 – 589, 2003.
- A. Kharrat and N. Benamrane, , “Evolutionary Support Vector Machine for Parameters Optimization Applied to Medical Diagnostic,” VISAPP 2011 - International Conference on Computer Vision Theory and Applications, Algeria, Oran, 2011.
- C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, 1995.
- M. Rychetsky, Algorithms and architectures for machine learning based on regularized neural networks and support vector approaches. Shaker Verlag GmBH, Germany, 2001.
- J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
- M.Govindarajan,"A Hybrid RBF-SVM Ensemble Approach for Data Mining Applications", International Journal of Intelligent Systems and Applications (IJISA), vol.6, no.3, pp.84-95, 2014. DOI: 10.5815/ijisa.2014.03.09.
- J. C. Platt, “Improvements to platt’s smo algorithm for svm classifier design,” Neural Computation, vol. 13, no. 3, 2001.
- J. C. Platt, B. Schlkopf, C. Burges, and A. Smola, “Fast training of support vector machines using sequential minimal optimization,” Advances in Kernel Methods - Support Vector Learning, 1999.
- G. W. Flake and S. Lawrence, “Efficient svm regression training with smo,” Journal Machine Learning, vol. 46, no. 3, 2002.
- J. A. K. Suykens, T. V. Gestel, J. D. Brabanter, B. D. Moor, and J. Vandewalle, “Least squares support vector machines,” World Scientific Pub. Co., Singapore, 2002.
- J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, vol. 9, 1999.
- J. Ye and T. Xiong, “Svm versus least squares svm,” Journal of Machine Learning Research, vol. 2, Oct 2007.
- L. Jiao, L. Bo, and L. Wang, “Fast sparse approximation for least squares support vector machine,” IEEE Transactions on Neural Transactions, vol. 19, no. 3, May 2007.
- J. S. Snchez, R. A. Mollineda, and J. M. Sotoca, “An analysis of how training data complexity affects the nearest neighbor classifiers,” Pattern Analysis and Applications, vol. 10, no. 3, 2007.
- M. Raniszewski, “Sequential reduction algorithm for nearest neighbor rule,” Computer Vision and Graphics, vol. 6375, 2010.
- D. Coomans and D. Massart, “Alternative k-nearest neighbor rule in supervised pattern recognition,” Analytica Chimica Acta, vol. 136, 1982.
