Year 2020, Volume , Issue 20, Pages 448 - 455 2020-12-31

Kronik Böbrek Hastalığının Erken Tanısı için Yeni Bir Klinik Karar Destek Sistemi
A New Clinical Decision Support System for Early Diagnosis of Chronic Kidney Disease

Can EYÜPOĞLU [1]


Kronik böbrek hastalığı dünya çapında bir sağlık sorunudur. Erken tanı ve tedavi sayesinde bu hastalığın ilerlemesini yavaşlatmak veya durdurmak mümkün olmaktadır. Klinik karar destek sistemleri, tıp doktorlarına klinik karar verme görevlerinde yardımcı olmak amacıyla tasarlanan sağlık bilgi teknolojisi sistemleridir. Bu çalışmada kronik böbrek hastalığının erken tanısı için yeni bir klinik karar destek sistemi önerilmiştir. Önerilen sistemin özellik çıkarma ve sınıflandırma aşamalarında sırasıyla temel bileşen analizi (principal component analysis-PCA) ve rastgele ormanlar (random forests-RF) teknikleri kullanılmıştır. Önerilen sistemin performansı, altı farklı performans metriği ile klasik makine öğrenmesi algoritmaları ve literatürde daha önce yapılan çalışmalar ile kıyaslanmıştır. Test sonuçları, önerilen sistemin başarılı olduğunu ve kronik böbrek hastalığının erken tanısı için karar vermede doktorlara destek olabileceğini göstermektedir.
Chronic kidney disease is a worldwide health problem. It is possible to slow or stop the progression of this disease thanks to early diagnosis and treatment. Clinical decision support systems are health information technology systems designed to assist medical doctors in clinical decision making tasks. In this study, a new clinical decision support system is proposed for the early diagnosis of chronic kidney disease. Principal component analysis (PCA) and random forests (RF) techniques are used in the feature extraction and classification phases of the proposed system, respectively. The performance of the proposed system has been compared with classical machine learning algorithms and previous studies in the literature using six different performance metrics. The test results show that the proposed system is successful and can assist doctors in making decisions for early diagnosis of chronic kidney disease.
  • Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine Learning, 6(1), 37-66.
  • Al-Hyari, A. Y., Al-Taee, A. M., & Al-Taee, M. A. (2013, December). Clinical decision support system for diagnosis and management of chronic renal failure. In 2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT) (pp. 1-6). IEEE.
  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
  • Cleary, J. G., & Trigg, L. E. (1995, July). K*: An instance-based learner using an entropic distance measure. In 12th International Conference on Machine Learning (pp. 108-114).
  • Cottrell, G. W., & Munro, P. (1988, October). Principal components analysis of images via back propagation. In Visual Communications and Image Processing'88: Third in a Series (pp. 1070-1077). International Society for Optics and Photonics.
  • Couser, W. G., Remuzzi, G., Mendis, S., & Tonelli, M. (2011). The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. Kidney International, 80(12), 1258-1270.
  • Eyupoglu, C., Aydin, M. A., Zaim, A. H., & Sertbas, A. (2018). An efficient big data anonymization algorithm based on chaos and perturbation techniques. Entropy, 20(5), 373.
  • Frank, E. (2014). Fully supervised training of Gaussian radial basis function networks in WEKA. Department of Computer Science, University of Waikato, Hamilton, New Zealand.
  • Freund, Y., & Schapire, R. E. (1996, July). Experiments with a new boosting algorithm. In 13th International Conference on Machine Learning (pp. 148-156).
  • Freund, Y., & Schapire, R. E. (1999). Large margin classification using the perceptron algorithm. Machine Learning, 37(3), 277-296.
  • Genkin, A., Lewis, D. D., & Madigan, D. (2007). Large-scale Bayesian logistic regression for text categorization. Technometrics, 49(3), 291-304.
  • Genuer, R., Poggi, J. M., & Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters, 31(14), 2225-2236.
  • Gupta, D., Khare, S., & Aggarwal, A. (2016, April). A method to predict diagnostic codes for chronic diseases using machine learning techniques. In 2016 International Conference on Computing, Communication and Automation (ICCCA) (pp. 281-287). IEEE.
  • Holte, R. C. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11(1), 63-90.
  • Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24(6), 417.
  • Jha, V., Garcia-Garcia, G., Iseki, K., Li, Z., Naicker, S., Plattner, B., Saran, R., Wang, A. Y. M., & Yang, C. W. (2013). Chronic kidney disease: global dimension and perspectives. The Lancet, 382(9888), 260-272.
  • John, G. H., & Langley, P. (1995, August). Estimating continuous distributions in Bayesian classifiers. In 10th Conference on Uncertainty in Artificial Intelligence (UAI’95) (pp. 338-345).
  • Jolliffe, I. T. (1986). Principal components in regression analysis. In Principal component analysis (pp. 129-155). Springer, New York, NY. Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., & Murthy, K. R. K. (2001). Improvements to Platt’s SMO algorithm for SVM classifier design. Neural Computation, 13(3), 637-649.
  • Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine Learning, 59(1-2), 161-205.
  • Le Cessie, S., & Van Houwelingen, J. C. (1992). Ridge estimators in logistic regression. Journal of the Royal Statistical Society: Series C (Applied Statistics), 41(1), 191-201.
  • Levey, A. S., & Coresh, J. (2012). Chronic kidney disease. The Lancet, 379(9811), 165-180.
  • National Kidney Foundation. (2020). Global Facts: About Kidney Disease. Retrieved from https://www.kidney.org/kidneydisease/global-facts-about-kidney-disease#
  • Ogunleye, A., & Wang, Q. G. (2018, June). Enhanced XGBoost-based automatic diagnosis system for chronic kidney disease. In 2018 IEEE 14th International Conference on Control and Automation (ICCA) (pp. 805-810). IEEE.
  • Ogunleye, A., & Wang, Q. G. (2019). XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Transactions on Computational Biology and Bioinformatics.
  • Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
  • Robbins, H., & Monro, S. (1951). A stochastic approximation method. The Annals of Mathematical Statistics, 22(3), 400-407.
  • Salekin, A., & Stankovic, J. (2016, October). Detection of chronic kidney disease and selecting important predictive attributes. In 2016 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 262-270). IEEE.
  • Smith, L. I. (2002). A tutorial on principal components analysis. Technical Report OUCS-2002-12, Department of Computer Science, University of Otago, New Zealand.
  • Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437.
  • Soundarapandian, P., Jerlin Rubini, L. & Eswaran, P. (2015). Chronic Kidney Disease Data Set [Data file]. Available from https://archive.ics.uci.edu/ml/datasets/chronic_kidney_disease
  • Wang, X., & Paliwal, K. K. (2003). Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition. Pattern Recognition, 36(10), 2429-2439.
  • Webster, A. C., Nagler, E. V., Morton, R. L., & Masson, P. (2017). Chronic kidney disease. The Lancet, 389(10075), 1238-1252.
  • World Health Organization. (2020). Mortality and global health estimates: Causes of death; Projections for 2015–2030; Projection of death rates. Retrieved from https://apps.who.int/gho/data/node.main
  • World Kidney Day. (2020). Chronic Kidney Disease. Retrieved from https://www.worldkidneyday.org/facts/chronic-kidney-disease/ Xun, L., Xiaoming, W., Ningshan, L., & Tanqi, L. (2010, October). Application of radial basis function neural network to estimate glomerular filtration rate in Chinese patients with chronic kidney disease. In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010) (pp. 332-335). IEEE.
  • Yavuz, E., & Eyupoglu, C. (2019). A cepstrum analysis-based classification method for hand movement surface EMG signals. Medical & Biological Engineering & Computing, 57(10), 2179-2201.
  • Yavuz, E., & Eyupoglu, C. (2020). An effective approach for breast cancer diagnosis based on routine blood analysis features. Medical & Biological Engineering & Computing.
  • Yavuz, E., Eyupoglu, C., Sanver, U., & Yazici, R. (2017). An ensemble of neural networks for breast cancer diagnosis. In 2017 International Conference on Computer Science and Engineering (UBMK) (pp. 538-543). IEEE.
  • Yavuz, E., & Eyüpoğlu, C. (2019). Meme Kanseri Teşhisi İçin Yeni Bir Skor Füzyon Yaklaşımı. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(3), 1045-1060.
Primary Language tr
Subjects Engineering
Journal Section Articles
Authors

Orcid: 0000-0002-6133-8617
Author: Can EYÜPOĞLU (Primary Author)
Institution: MİLLİ SAVUNMA ÜNİVERSİTESİ, HAVA HARP OKULU
Country: Turkey


Dates

Publication Date : December 31, 2020

APA Eyüpoğlu, C . (2020). Kronik Böbrek Hastalığının Erken Tanısı için Yeni Bir Klinik Karar Destek Sistemi . Avrupa Bilim ve Teknoloji Dergisi , (20) , 448-455 . DOI: 10.31590/ejosat.743652