Makine Öğrenmesi Yöntemleri ile Böbrek Yetmezliği Hastalığını Etkileyen Faktörlerin Sınıflandırılması

Yıl 2020, Cilt: 36 Sayı: 1, 88 - 101, 26.04.2020

Pelin Kasap Burçin Şeyda Çorba Zorlu

Öz

Makine
öğrenmesi yöntemleri, sağlık araştırmalarında veri analizi için yaygın olarak
kullanılmaktadır. Bu çalışmanın amacı, Yapay Sinir Ağları (Çok Katmanlı Algılayıcı),
Destek Vektör Makineleri, Naive Bayes, Karar Ağaçları, Rastgele Orman
Algoritması, K-En Yakın Komşu Algoritması gibi çeşitli makine öğrenmesi
yöntemlerini kullanarak böbrek yetmezliğini etkileyen faktörleri
sınıflandırmaktır. Bu çalışmada, Ankara Numune Hastanesi’nde acil servise
gelen, 18 yaşından büyük ve üst gastrointestinal kanama belirtileri bulunan 237
hasta seçilmiştir. Burada makine öğrenmesi yöntemleri ile sınıflandırma yapmak
için böbrek yetmezliğini etkileyen yaş, cinsiyet, kan değerleri, diğer
hastalıklar vb. gibi 34 değişken kullanılmıştır. Makine öğrenmesi yöntemleri
doğruluk oranları, tahmin, duyarlılık, özgüllük ve Kappa değerlerine göre
karşılaştırıldığında, karar ağaçları algoritmasının iyi performans gösterdiği
bulunmuştur.

Anahtar Kelimeler

Makine Öğrenmesi, Sınıflandırma, Karar Ağaçları, Böbrek Yetmezliği

Classification of Factors Affecting Renal Failure by Machine Learning Methods

Öz

Machine
learning methods are widely used for data analysis in health research. The aim
of this study is to classify the factors that affect renal failure by using
various machine learning methods such as Artificial Neural Networks (Multilayer
Perceptron), Support Vector Machines, Naive Bayes, Decision Trees, Random
Forests, K-Nearest Neighborhood algorithms. In this study, 237 patients who
have been in emergency unit in Hospital of Numune in Ankara and were older than
18 years and have upper gastrointestinal bleeding symptoms have been selected. Here, 34 variables such as age, gender, blood values,
other diseases etc. which affect renal failure have been used to make
classification with machine learning methods. When machine learning
methods are compared according to the accuracy rates, precision, sensivity, specifity and Kappa
values, it has been
found that decision trees algorithm performs well.

Anahtar Kelimeler

Machine Learning, Classification, Decision Trees, Renal Failure

Kaynakça

• [1] Schultz, M., Reitmann, S. 2019. Machine learning approach to predict aircraft boarding. Transportation Research Part C: Emerging Technologies, 98, 391-408.
• [2] Maheshwari, A., Davendralingam, N., DeLaurentis, D. A. 2018. A Comparative Study of Machine Learning Techniques for Aviation Applications. In 2018 Aviation Technology, Integration, and Operations Conference p. 3980.
• [3] Gümüşçü, A., Tenekeci, M. E., Bilgili, A. V. 2019. Estimation of wheat planting date using machine learning algorithms based on available climate data. Sustainable Computing: Informatics and Systems.
• [4] Rehman, T. U., Mahmud, M. S., Chang, Y. K., Jin, J., Shin, J. 2019. Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture, 156, 585-605.
• [5] Burri, R. D., Burri, R., Bojja, R. R., Buruga, S. 2019. Insurance Claim Analysis using Machine Learning Algorithms. International Journal of Advanced Science and Technology, 127(1), 147-155.
• [6] Ferreiro, S., Sierra, B., Irigoien, I., Gorritxategi, E. 2011. Data mining for quality control: Burr detection in the drilling process. Computers & Industrial Engineering, 60(4), 801-810.
• [7] Adadi, A., Adadi, S., Berrada, M. 2019. Gastroenterology Meets Machine Learning: Status Quo and Quo Vadis. Advances in Bioinformatics.
• [8] Librenza-Garcia, D., Kotzian, B. J., Yang, J., Mwangi, B., Cao, B., Lima, L. N. P. Passos, I. C. 2017. The impact of machine learning techniques in the study of bipolar disorder: a systematic review. Neuroscience & Biobehavioral Reviews, 80, 538-554.
• [9] Lofaro, D., Maestripieri, S., Greco, R., Papalia, T., Mancuso, D., Conforti, D., Bonofiglio, R. 2010. Prediction of chronic allograft nephropathy using classification trees. In Transplantation proceedings, Vol. 42, No. 4, pp. 1130-1133, Elsevier.
• [10] Greco, R., Papalia, T., Lofaro, D., Maestripieri, S., Mancuso, D., Bonofiglio, R. 2010. Decisional trees in renal transplant follow-up. In Transplantation proceedings, Vol. 42, No. 4, pp. 1134-1136, Elsevier.
• [11] Martínez-Martínez, J. M., Escandell-Montero, P., Barbieri, C., Soria-Olivas, E., Mari, F., Martínez-Sober, M. Stopper, A. 2014. Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques. Computer methods and programs in biomedicine, 117(2), 208-217.
• [12] Mezzatesta, S., Torino, C., De Meo, P., Fiumara, G., Vilasi, A. 2019. A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis. Computer Methods and Programs in Biomedicine, 177, 9-15.
• [13] Cruz, J. A., Wishart, D. S. 2006. Applications of machine learning in cancer prediction and prognosis. Cancer informatics, 2, 117693510600200030.
• [14] Tangri, N., Ansell, D., Naimark, D. 2011. Determining factors that predict technique survival on peritoneal dialysis: application of regression and artificial neural network methods. Nephron Clinical Practice, 118(2), c93-c100.
• [15] Kumari, M., Godara, S. 2011. Comparative study of data mining classification methods in cardiovascular disease prediction 1, International Journal of Computer Science and Technology, Vol 2, Issue 2, 304-308.
• [16] Gupta, S., Kumar, D., Sharma, A. 2011. Data Mining Classification Techniques Applied for Breast Cancer Diagnosis and Prognosis. Indian Journal of Computer Science and Engineering, 2 (2), 188-195.
• [17] Krishnaiah, V., Narsimha, D. G., Chandra, D.N.S. 2013. Diagnosis of lung cancer prediction system using data mining classification techniques. International Journal of Computer Science and Information Technologies, 4(1), 39-45.
• [18] Kunwar, V., Chandel, K., Sabitha, A. S., Bansal, A. 2016. Chronic Kidney Disease analysis using data mining classification techniques. In 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 300-305. IEEE.
• [19] Aziz, A., Rehman, A. U. 2017. Detection of Cardiac Disease using Data Mining Classification Techniques. IJACSA) International Journal of Advanced Computer Science and Applications, 8(7).
• [20] Davazdahemami, B., Delen, D. 2019. The confounding role of common diabetes medications in developing acute renal failure: A data mining approach with emphasis on drug-drug interactions. Expert Systems with Applications, 123, 168-177.
• [21] Kumar, A., Kumar, A., Kumar, P., Kumar, P. 2014. U.S. Patent No. 8,668,938. Washington, DC: U.S. Patent and Trademark Office.
• [22] Podestà, M. A., Galbusera, M., Remuzzi, G. 2019. Bleeding and Hemostasis in Acute Renal Failure. In Critical Care Nephrology (pp. 630-635). Content Repository Only!.
• [23] Lew, S. Q., Ing, T. S. 2019. Gastrointestinal Problems in Acute Kidney Injury. In Critical Care Nephrology pp. 635-640, Content Repository Only!.
• [24] Gupta, S., Kumar, D., Sharma, A. 2011. Data mining classification techniques applied for breast cancer diagnosis and prognosis. Indian Journal of Computer Science and Engineering (IJCSE), 2(2), 188-195.
• [25] Qiu, X.Y., Kang, K., Zhang, H.X. 2008. Selection of kernel parameters for K-NN, IEEE International Joint Conference on Neural Networks (IJCNN), 61-65.
• [26] Peterson, L. E. 2009. K-nearest neighbor. Scholarpedia, 4(2), 1883.
• [27] Alpaydin, E. 2004. Introduction to Machine Learning, MIT Press.
• [28] Islam, M. J., Wu, Q. J., Ahmadi, M., Sid-Ahmed, M. A. 2007. Investigating the performance of naive-bayes classifiers and k-nearest neighbor classifiers. November, 2007. 2007 International Conference on Convergence Information Technology (ICCIT 2007), 1541-1546. IEEE.
• [29] Dangare, C. S., Apte, S. S. 2012. Improved study of heart disease prediction system using data mining classification techniques. International Journal of Computer Applications, 47(10), 44-48.
• [30] Aksu, M. Ç., Karaman, E. 2017. Karar Ağaçları ile Bir Web Sitesinde Link Analizi ve Tespiti. Acta Infologica, 1 (2), 84-91.
• [31] Phyu, N. T. 2009. Survey of Classification Techniques in Data Mining. IMECS 2009, March 18-20, Hong Kong.
• [32] Breiman, L. 2001. Random Forests. Machine Learning, 45(1), 5- 32. doi: 10.1023/A:1010933404324
• [33] Breiman, L., & Cutler, A. 2005. Random Forests. Berkeley.
• [34] Han, J., Pei, J., Kamber, M. 2011. Data mining: concepts and techniques. Elsevier.
• [35] Pal, M. 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26 (1), 217–222.
• [36] Akman, M., Genç, Y., Ankaralı, H. 2011. Random Forests Methods and an Application in Health Science. Turkiye Klinikleri Journal of Biostatistics, 3(1), 36-48.
• [37] Karakoyun, M., Hacıbeyoğlu, M. 2014. Biyomedikal Veri Kümeleri ile Makine Öğrenmesi Sınıflandirma Algoritmalarının İstatistiksel Olarak Karşılaştırılması. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 16 (48), 30-42.
• [38] Lewis, D. D. 1992. An evaluation of phrasal and clustered representations on a text categorization task. In Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, June, ACM, 37-50.
• [39] Dimitoglu, G., Adams, J. A., Jim, C. M. 2012. Comparison of the C4. 5 and a Naïve Bayes classifier for the prediction of lung cancer survivability. arXiv preprint arXiv, 1206.1121.
• [40] Orhan, U., Adem, K. 2012. Naive Bayes Yönteminde Olasılık Çarpanlarının Etkileri. Elektrik Elektronik ve Bilgisayar Mühendisliği Sempozyumu, 723.
• [41] Haykin, S. 1999. Neural networks: A comprehensive foundation, Prentice-Hall, New Jersey.
• [42] Kumari, M. and Godara, S. 2011. Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction. International Journal of Computer Science and Technology, 2 (2), 304-308.
• [43] Öztemel, E. 2012. Yapay Sinir Ağları. 3nd, Papatya Yayıncılık, İstanbul.
• [44] Kartolopoulos, S. V. 1996. Understanding neural network and fuzzy logic, IEEE Press., New York.
• [45] Vapnik, V.N. 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
• [46] Han, J., Kamber, M. 2006. Data Mining Concepts and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco.
• [47] Güner, N., Çomak, E. 2011. Mühendislik Öğrencilerinin Matematik I Derslerindeki Başarısının Destek Vektör Makineleri Kullanılarak Tahmin Edilmesi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 17 (2), 87-96.
• [48] Huang, T. M., Kecman, V., Kopriva, I. 2006. Kernel Based Algorithms For Mining Huge Data Sets: Supervised, Semi-Supervised, and Unsupervised Learning. Studies in Computational Intelligence, Secaucus, NJ, Springer, USA.
• [49] Tan, Y., Wang, J. 2004. A support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension. IEEE Transactions on knowledge and data engineering, 16(4), 385-395.
• [50] Cover, T., Hart, P., 1967. Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1), 21-27.