Classification of non-chronic and chronic kidney disease using SVM neural networks

 
 
 
  • Abstract
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  • Abstract


    Chronic kidney disease (CKD) refers to the failure of the renal functionalities that leads to the deposition of wastes, electrolytes and other fluids in the body. It is very important to recognize the symptoms that cause the CKD and pathological blood and urine test indicates the key attributes. It is well fact that one has to undergo dialysis due to renal failure. The severity level of disease can be predicted as well as classified using appropriate computer aided quantitative tools. This specific study discusses the classification of chronic and non-chronic kidney disease NCKD using support vector machine (SVM) neural networks. The simulation study makes use of UCI repository CKD datasets with n=400. In order to train to train the attributes of kidney dialysis four cases were considered by including the nominal and numerical values. A radical basis kernel function was employed to train SVM. The performance of the proposed scheme is evaluated in terms of the sensitivity, specificity and classification accuracy. Results reveal an overall classification accuracy of 94.44% was obtained by combining 6 attributes. It can be concluded that the SVM based approach found to be a potential candidate for classification of CKD and NCKD.


  • Keywords


    Hemodialysis; CKD; NCKD; SVM

  • References


      [1] Liao, Min-Tser; Sung, Chih-Chien; Hung, Kuo-Chin; Wu, Chia-Chao; Lo, Lan; Lu, Kuo-Cheng (2012). "Insulin Resistance in Patients with Chronic Kidney Disease". Journal of Biomedicine and Biotechnology. 2012

      [2] What Is Chronic Kidney Disease?". National Institute of Diabetes and Digestive and Kidney Diseases. June 2017. Accessed 11 November 2017.

      [3] V. A. Moyer, “Screening for chronic kidney disease: Us preventive services task force recommendation statement,” Annals of internal medicine, vol. 157, no. 8, pp. 567–570, 2012.

      [4] L. C. Plantinga, D. S. Tuot, and N. R. Powe, “Awareness of chronic kidney disease among patients and providers,” Advances in chronic kidney disease, vol. 17, no. 3, pp. 225–236, 2010.

      [5] Redmon JH, Elledge MF, Womack DS, Wickremashinghe R, Wanigasuriya KP, Peiris-John RJ, Lunyera J, Smith K, Raymer JH, Levine KE (2014). "Additional perspectives on chronic kidney disease of unknown aetiology (CKDu) in Sri Lanka – lessons learned from the WHO CKDu population prevalence study". BMC Nephrology. 15 (1): 125. doi:10.1186/1471-2369-15-12

      [6] Abeer Y. Al-Hyari,Ahmad M. Al-Taee ,Majid A. Al-Taee, Diagnosis and Classification of Chronic Renal Failure Utilising Intelligent Data Mining Classifiers, International Journal of Information Technology and Web Engineering, Volume 9 Issue 4, October 2014,Pages 1-12

      [7] José Neves ., M. Rosário Martins ., João Vilhena .,João Neves ., Sabino Gome.,António Abelha., José Machado.,Henrique Vicente, A Soft Computing Approach to Kidney Diseases Evaluation, J Med Syst (2015) 39: 131,DOI 10.1007/s10916-015-0313-4

      [8] Naganna Chetty, et.al, Role of Attributes Selection in Classification of Chronic Kidney Disease Patients, IEEE conference, pp. 82-87, 2015

      [9] M. Baumgarten and T. Gehr, “Chronic kidney disease: detection and evaluation,” American family physician, vol. 84, no. 10, p. 1138, 2011.

      [10] Levey AS, de Jong PE, Coresh J, El Nahas M, Astor BC, Matsushita K, et al. The definition, classification,and prognosis of chronic kidney disease: a KDIGO Controversies Conference report. Kidney Int,2011;80:17–28.

      [11] Balasaravanan, K. "Detection of dengue disease using artificial neural network based classification technique." International Journal of Engineering & Technology 7, no. 1.3 (2017): 13-15.

      [12] Kusiak, A., Dixon, B., & Shah, S., Predicting survival time for kidney dialysis patients. Computersin Biology and Medicine, 35(4), 311–327. doi:10.1016/j.compbiomed.2004.02.004 PMID:15749092,2005.

      [13] N. Sriraam, V. Natasha and H. Kaur,” data mining approaches forkidney dialysis treatment”, Journal of Mechanics in Medicine and Biology, Volume 06, Issue 02, June 2006.

      [14] N Sriraam, V Natasha, H Kaur,Data Mining Techniques and MedicalDecision Making for Urological Dysfunction,Handbook of Research on Informatics in Healthcare and Biomedicine,2006

      [15] Sai Prasad Potharaju &M. Sreedevi, An Improved Prediction of Kidney Disease using SMOTE, Indian Journal of Science and Technology, Vol 9(31), DOI: 10.17485/ijst/2016/v9i31/95634, August 2016

      [16] Murat Kolu, Kemal Tutuncu, Classification of chronic disease with most known data mining methods

      [17] Sinha, P, & Siha, P. (2015). Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM. In International Journal of Engineering Research and
      Technology, Vol. 4, No. 12, ESRSA Publications.

      [18] Kumar M. (2016). Prediction of Chronic Kidney Disease Using Random Forest Machine Learning Algorithm, Journal of Computer Science and Mobile Computing, Vol.5 Issue.2, p. 24-33.

      [19] Made Satria Wibawa, Made Dendi Maysanjaya, I Made Agus Wirahadi Putra, "Boosted classifier and features selection for enhancing chronic kidney disease diagnose", Cyber and IT Service Management(CITSM)20175th International Conference on, pp. 1-6, 2017.

      [20] Ravindra B V , Sriraam. N ,and Ms. Geetha Mayya.Discovery of significant parameters in Kidney dialysis data sets by K-means algorithms International conference Proc. of International Conference on Circuits, Communication, Control and Computing (I4C),ISBN :978-1-4799 pp.452-454,2014

      [21] B.V. Ravindra, N. Sriraam, Geetha Maiya,Finding Impact of Precedence based Critical Attributes in Kidney Dialysis Data Set using Clustering Technique,,International Journal of Biomedical and Clinical Engineering (IJBCE),Vol.4,issue1, pp.44-50,Jan-June 2015.

      [22] Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

      [23] L.Jerlin Rubini, et.al “Generating comparative analysis of early stage prediction of Chronic Kidney Disease”, International Journal Of Modern Engineering Research (IJMER) Vol. 5, pp 50-55 2015

      [24] V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.

      [25] Vapnik VN. An overview of statistical learning theory. IEEETrans Neural Network[

      [26] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, 2000


 

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Article ID: 10669
 
DOI: 10.14419/ijet.v7i1.3.10669




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