PREDICTION OF CHRONIC KIDNEY DISEASE USING ROTATION FOREST CLASSIFICATION ALGORITHM

Year 2019, Issue: 043, 21 - 34, 31.12.2019

Serhat Kılıçarslan , Mete Çelik

Abstract

Chronic kidney disease (CBR) has increased in recent years by affecting the lives of people adversely. It affects kidneys and prevents them doing their normal duties properly. Without early diagnosis and treatment of CBR, it can trigger diseases such as high blood pressure, heart disease, diabetes mellitus and kidney failure and it can even cause deaths. For this reason, it is important to diagnose and predict CBR early. In the literature, various heuristic and non-heuristic data mining classification techniques have been applied on predicting CBR. In this study, it is proposed to use the rotation forest algorithm as a non-heuristic collective data mining method for predicting CBR. Experimental evaluations show that the proposed approach performs better than other algorithms on predicting CBR.

Keywords

Chronic Kidney Disease, Rotation Forest Algorithm, Data Mining

ROTASYON ORMAN SINIFLANDIRMA ALGORİTMASI KULLANARAK KRONİK BÖBREK RAHATSIZLIĞININ TAHMİNİ

Abstract

Kronik böbrek rahatsızlığı (KBR) son günlerde artarak insanların yaşamını olumsuz etkileyen ve böbreklere zarar vererek normal görevlerini uzun süre yapmalarını engelleyen bir rahatsızlıktır. KBR'nin erken tanı ve tedavisi yapılmaz ise yüksek tansiyon, kalp rahatsızlığı, şeker rahatsızlığı, böbrek yetmezliği gibi hastalıkları da tetikleyebilmekte ve rahatsızlığa bağlı ölümler artabilmektedir. Bu nedenle kronik böbrek rahatsızlığının teşhis ve tahmininin erken yapılması önemlidir. Literatürde KBR tahmini için sezgisel ve sezgisel olmayan veri madenciliği teknikleri uygulanmıştır. Bu çalışmada KBR'nin tahmini için sezgisel olmayan kolektif veri madenciliği yöntemlerinden olan rotasyon orman algoritmasınının kullanılması önerilmiştir. Deneysel sonuçlar önerilen yaklaşımın, kronik böbrek rahatsızlığını tahmin etmede, diğer algoritmalarından daha iyi performans sergilediğini göstermiştir.

Keywords

Kronik Böbrek Rahatsızlığı, Rotasyon Orman Algoritması, Veri Madenciliği

References

• [1] Erdem, B. K., & Akbas, H., (2017), Kronik Böbrek Hastalığı ve Vasküler Kalsifikasyon, Türk Klinik Biyokimya Derg. , 152: 89-98.
• [2] Singh, P., Chandola, V., & Fox, C., (2017), Automatic Extraction of Deep Phenotypes for Precision Medicine in Chronic Kidney Disease, Proceedings of the 2017 International Conference on Digital Health - DH ’17, 195–199. https://doi.org/10.1145/3079452.3079489
• [3] Topbaş, E., (2015), Kronik Böbrek Hastalığının Önemi , Evreleri Ve Evrelere Özgü Bakımı, Nefroloji Hemşireliği Dergisi, 53-59.
• [4] Dua, D. and 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.
• [5] İlkuçar, M., (2015), Kronik Böbrek Hastalarının Yapay Sinir Ağı ve Radyal Temelli Fonksiyon Ağı ile Teşhisi. Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(2), 82-88..06.2018).
• [6] Jena, L., & Kamila K. N., (2015), Distributed Data Mining Classification Algorithms for Prediction of Chronic-Kidney-Disease, International Journal of Emerging Research in Management &Technology, 93594, 2278–9359.
• [7] Rubini, L. J., & Eswaran, P., (2015), Generating comparative analysis of early stage prediction of Chronic Kidney Disease. International Journal of Modern Engineering Research (IJMER), 5(7), 49-55.
• [8] Yildirim, P., (2017), Chronic Kidney Disease Prediction on Imbalanced Data by Multilayer Perceptron: Chronic Kidney Disease Prediction, 2017 IEEE 41st Annual Computer Software and Applications Conference COMPSAC, 193–198.
• [9] Ramya, S., & Radha, N., (2016), Diagnosis of chronic kidney disease using machine learning algorithms, International Journal of Innovative Research in Computer and Communication Engineering, 4(1), 812-820.
• [10] Kunwar, V., Chandel, K., Sabitha, A. S., & Bansal, A., (2016), Chronic Kidney Disease analysis using data mining classification techniques, In IEEE 6th International Conference Cloud System and Big Data Engineering (Confluence), 300-305.
• [11] Vijayarani, S., & Dhayanand, S., (2015), Data Mining Classification Algorithms for Kidney Disease Prediction, International Journal on Cybernetics & Informatics, 44, 13–25.
• [12] Kumar, M., (2016), Prediction of Chronic Kidney Disease Using Random Forest Machine Learning Algorithm Running Title: Prediction of Chronic Kidney Disease, International Journal of Computer Science and Mobile Computing, 52522, 24–33.
• [13] Balakrishna, T., Narendra, B., Reddy, M. H., & Jayasri, D., (2017), Diagnosis of Chronic Kidney Disease Using Random Forest Classification Technique, HELIX, 7(1), 873-877.
• [14] Baby, P. S., & Vital, P., (2015), Statistical Analysis and Predicting Kidney Diseases using Machine Learning Algorithms, International Journal of Engineering Research & Technology, 407, 206–210.
• [15] 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.
• [16] Sinha, P., (2015), Comparative study of chronic kidney disease prediction using KNN and SVM, International Journal of Engineering Research and Technology, 4(12), 608-12.
• [17] Eroğlu K.ve Palabaş T., (2016), Kronik Böbrek Hastalığı Tespitinde Farklı Sınıflandırma Yöntemleri ve Farklı Topluluk Algoritmalarının Birlikte Kullanımının Sınıflandırma Performansına Etkisi, Elektrik-Elektronik Mühendisliği Odası, 512–516.
• [18] Cheng, L. C., Hu, Y. H., & Chiou, S. H., (2017), Applying the Temporal Abstraction Technique to the Prediction of Chronic Kidney Disease Progression, Journal of medical systems, 41(5), 85.
• [19] 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.
• [20] Rodríguez, J. J., Kuncheva, L. I., & Alonso, C. J., (2006), Rotation forest: A New classifier ensemble method, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2810, 1619–1630.
• [21] Akçetin, E., & Çelik, U., (2014), İstenmeyen Elektronik Posta (Spam) Tespitinde Karar Ağacı Algoritmalarının Performans Kıyaslaması, Journal of Internet Applications & Management/İnternet Uygulamaları ve Yönetimi Dergisi, 5(2).
• [22] Namlı, Ö. H., & Özcan, T., (2017), Makine Öğrenmesi Algoritmaları Kullanarak Gişe Hasılatının Tahmini, Yönetim Bilişim Sistemleri Dergisi, 3(2), 130-143.
• [23] Ali, J., Khan, R., Ahmad, N., & Maqsood, I., (2012), Random forests and decision trees, IJCSI International Journal of Computer Science Issues, 9(5), 272-278.
• [24] Breiman, L., (2001), Random forests. Machine Learning, 451, 5–32.
• [25] Onan, A., (2015), Şirket İflaslarının Tahminlenmesinde Karar Ağacı Algoritmalarının Karşılaştırmalı Başarım Analizi, Bilişim Teknolojileri Dergisi, 81, 9–19.
• [26] Sebban, M., Nock, R., Chauchat, J. H., & Rakotomalala, R., (2000), Impact of learning set quality and size on decision tree performances. International Journal of Computers, Systems and Signals, 11, 85–105.
• [27] Fawcett, T., (2006), An introduction to ROC analysis, Pattern recognition letters, 27(8), 861
• [28] Türkiye Halk Sağlığı Kurumu, Türkiye Böbrek Hastalıkları Önleme ve Kontrol Programı Eylem Planı, (2014-2017). Sağlık Bakanlığı, Yayın No: 946, Ankara, 2014, ss. 1. http://www.tsn.org.tr/pdf/Turkiye_Bobrek_Hastaliklari_Onleme_ve_Kontrol_Programi.pdf (E.T. 08.06.2018).
• [29] Horspool S., (2016), Global Burden of Disease Study 2015 outlines chronic kidney disease as a cause of death worldwide. https://www.theisn.org/news/item/2969-global-burden-of-disease-study-2015-outlines-chronic-kidney-disease-as-a-cause-of-death-worldwide (Erişim Tarihi: 20.06.2018).
• [30] Kunwar, V., Chandel, K., Sabitha, A. S., & Bansal, A., (2016), Chronic Kidney Disease analysis using data mining classification techniques. In Cloud System and Big Data Engineering (Confluence), 2016 6th International Conference(pp. 300-305). IEEE.
• [31] Vapnik, V., (1995), “The nature of statistical learning theory,” Springer-Verlag: New York, pp. 75-100.
• [32] Orhan, U., Adem, K., & Comert, O., (2012), Least squares approach to locally weighted naive Bayes method. Journal of New Results in Science, 1(1).
• [33] Breiman, L., (2001), Random forests. Machine learning, 45(1), 5-32.
• [34] Hu, W., Hu, W., & Maybank, S., (2008), Adaboost-based algorithm for network intrusion detection. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(2), 577-583.
• [35] Melville, P., & Mooney, R. J., (2003), Constructing diverse classifier ensembles using artificial training examples. In IJCAI, Vol. 3, pp. 505-510.
• [36] Witten, I. H., Frank, E., & Hall, M. A., (2005), Data mining: Practical machine learning tools and techniques, (morgan kaufmann series in data management systems). Morgan Kaufmann, June, 104, 113.
• [37] Pasha, M., & Fatima, M., (2017), Comparative Analysis of Meta Learning Algorithms for Liver Disease Detection. Journal of Software, 12(12), 923-934.
• [38] Bagnall, A., Bostrom, A., Cawley, G., Flynn, M., Large, J., & Lines, J., (2018), Is rotation forest the best classifier for problems with continuous features?. arXiv preprint arXiv:1809.06705.
• [39] Hosseinzadeh, M., & Eftekhari, M., (2015), Improving rotation forest performance for imbalanced data classification through fuzzy clustering. In Artificial Intelligence and Signal Processing (AISP), 2015 International Symposium on (pp. 35-40). IEEE.
• [40] Mauša, G. O. R. A. N., Bogunović, N., Grbac, T. G., & Bašić, B. D., (2015), Rotation forest in software defect prediction. SQAMIA, 1375, 35-43.
• [41] Coşkun, C., & Baykal, A., (2011), Veri Madenciliğinde Sınıflandırma Algoritmalarının Bir Örnek Üzerinde Karşılaştırılması. Akademik Bilişim, 2011, 1-8.
• [42] Qi, Y., (2012), Random forest for bioinformatics. In Ensemble machine learning (pp. 307-323). Springer, Boston, MA.
• [43] Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R., (2006), Random forests for land cover classification. Pattern Recognition Letters, 27(4), 294-300.
• [44] Pal, M., (2005), Random Forest Classifier For Remote Sensing Classification, International Journal Of Remote Sensing, 26(1) , 217-222.
• [45] Veerappan, I., & Abraham, G., (2013), Chronic kidney disease: Current status, challenges and management in India. Ch, 130, 593-7.
• [46] Go, A. S., Chertow, G. M., Fan, D., McCulloch, C. E., & Hsu, C. Y., (2004), Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. New England Journal of Medicine, 351(13), 1296-1305.
• [47] Tan, P. N., (2007), Introduction to data mining. Pearson Education India.
• [48] Garg, A. X., Adhikari, N. K., McDonald, H., Rosas-Arellano, M. P., Devereaux, P. J., Beyene, J. & Haynes, R. B., (2005), Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. Jama, 293(10), 1223-1238.