Diagnosis of Chronic Kidney Disease using Random Subspace Method with Particle Swarm Optimization

Year 2018, Volume: 10 Issue: 3, 1 - 5, 31.12.2018

Kemal Adem

https://doi.org/10.29137/umagd.472881

Abstract

Late diagnosis of chronic kidney disease, a
disease that has increased in recent years and threatens human life, may lead
to dialysis or kidney failure. In this study, kNN, SVM, RBF and Random subspace
data mining methods were applied on the data set consisting of 400 samples and
24 attributes taken from UCI for classification of chronic kidney disease with particle
swarm optimization (PSO) based feature selection method. As a result of the
study, the results of the application of each data mining method are compared
with the resultant training and test results. As a result of the comparison, it
was seen that the method of PSO feature selection affects the classification
success positively. Moreover, as a method of data mining, it has been seen that
the random subspace method has higher accuracy rates than the other methods.

Keywords

Chronic kidney disease, Particle Swarm Optimization, Random Subspace

References

• Liao, M., Sung, C., Hung, K., Wu, C., Lo, L., & Lu, K. (2012). Insulin Resistance in Patients with Chronic Kidney Disease. Journal of Biomedicine and Biotechnology.
• Moyer, V. A. (2012). Screening for chronic kidney disease: Us preventive services task force recommendation statement. Annals of internal medicine, vol. 157, no. 8, pp. 567–570.
• Plantinga, L. C., Tuot, D. S., & Powe, N. R. (2010). Awareness of chronic kidney disease among patients and providers. Advances in chronic kidney disease, vol. 17, no. 3, pp. 225–236.
• Witten, I. H., & Frank, E. (2005). Data Mining Practical Machine Learning Tools and Techniques. 2nd ed., San Francisco/ABD.
• Karakoyun, M., & Hacıbeyoğlu M. (2014). Biyomedikal veri kümeleri kullanarak makine öğrenmesi sınıflandırma algoritmalarının karşılaştırılması. 2014 October 9-10 [Akıllı Sistemlerde Yenilikler ve Uygulamaları (ASYU) Sempozyumu. İzmir/Turkey].
• Sunil, D., & Sowmya, B. P. (2017). Chronic Kidney Disease Analysis using Data Mining.
• Rubini, L. J., & Eswaran, P. (2015). Generating comparative analysis of early stage prediction of Chronic Kidney Disease. Journal Of Modern Engineering Research, 5(7), 49-55.
• Kumar, M. (2016). Prediction of Chronic Kidney Disease Using Random Forest Machine Learning Algorithm. International Journal of Computer Science and Mobile Computing, 5(2), 24-33.
• Polat, H., Mehr, H. D., & Cetin, A. (2017). Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods, Journal of medical systems, 41(4), 55.
• Başar, M. D., Sarı, P., Kılıç, N., & Akan, A. (2016). Detection of chronic kidney disease by using Adaboost ensemble learning approach, In IEEE Signal Processing and Communication Application Conference (SIU), 2016 24th . 773-776.
• Ravindra, B. V., Sriraam, N., & Geetha, M. (2018). Classification of non-chronic and chronic kidney disease using SVM neural networks. International Journal of Engineering & Technology, 7(1.3), 191-194.
• Kayaalp, F., Basarslan, M. S., & Polat, K. (2018). A hybrid classification example in describing chronic kidney disease. In 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT) (pp. 1-4). IEEE.
• Dua, D., & Karra Taniskidou, E. 2017. UCI Machine Learning Repository [http://archive.ics.uci. edu/ml. Irvine, CA: University of California, School of Information and Computer Science.
• Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
• Kennedy, J., & Eberhart, R. C. (1999). The particle swarm: social adaptation in information-processing systems. In New ideas in optimization (pp. 379-388). McGraw-Hill Ltd., UK.
• Ho T.K. (1998). The Random Subspace Method for Constructing Decision. IEEE Transactions on Pattern Analysis and Machine Intelligence, Lucent Tech no l., AT&T Bell Labs., Murray Hill, 20(8): 832 – 844.
• Marina, S. (2002). Bagging, boosting and the random subspace method for linear classifiers. Pattern Analysis and Applications. 5(2), 121–135.
• Tremblay, G. (2004). Optimizing nearest neighbour in random subspaces using a multi-objective genetic algorithm. 17th International Conference on Pattern Recognition. p. 208–11. Crossref
• Dacheng, T, Xiaoou, T, Xuelong, L, & Xindong W. (2006). Asymmetric bagging and random subspace for support vector machinesbased relevance feedback in image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(7), 1088-99.
• Kayal, P., & Kannan, S. (2017). An Ensemble Classifier Adopting Random Subspace Method based on Fuzzy Partial Mining. Indian Journal of Science and Technology, 10(12), 1-8.
• Metz, C. E. (1978). Basic principles of ROC analysis. In Seminars in nuclear medicine (Vol. 8, No. 4, pp. 283-298). WB Saunders.
• Gujarati, N. D. (1999). Temel Ekonometri. Çev. Ümit Şenesen ve Gülay G. Şenesen. 4. Baskı, 401-674, Literatür Yayınları, İstanbul.
• Rey, T, Kordon, A, & Wells, C. (2012). Applied Data Mining for Forecasting Using SAS. SAS Institute Inc, USA, 2012.