Abstract
Kidney failure disease is being observed as a serious challenge to the medical field with its impact on a massive population of the world. Devoid of symptoms, kidney diseases are often identified too late when dialysis is needed urgently. Advanced data mining technologies can help provide alternatives to handle this situation by discovering hidden patterns and relationships in medical data. The objective of this research work is to predict kidney disease by using multiple machine learning algorithms that are Support Vector Machine (SVM), Multilayer Perceptron (MLP), Decision Tree (C4.5), Bayesian Network (BN) and K-Nearest Neighbour (K-NN). The aim of this work is to compare those algorithms and define the most efficient one(s) on the basis of multiple criteria. The database used is “Chronic Kidney Disease” implemented on the WEKA platform. From the experimental results, it is observed that MLP and C4.5 have the best rates. However, when compared with Receiver Operating Characteristic (ROC) curve, C4.5 appears to be the most efficient.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
World Kidney Day: Chronic Kidney Disease. http://www.worldkidneyday.org/faqs/chronic-kidney-disease/(2015)
Jha, V., Garcia-Garcia, G., Iseki, K., et al.: Chronic kidney disease: global dimension and perspectives. Lancet 382(9888), 260–272 (2013)
Levey, A.S., Atkins, R., Coresh, J., et al.: Chronic kidney disease as a global public health problem: approaches and initiatives—a position statement from Kidney disease improving global outcomes. Kidney Int. 72(3), 247–259 (2007)
Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K.: Data mining in healthcare and biomedicine: a survey of the literature. J. Med. Syst. 36(4), 2431−2448 (2012)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, 200 pp. Cambridge University Press (2013). http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511801389]
Rahman, R.M., Md. Hasan, F.R.: Using and comparing different decision tree classification techniques for mining ICDDR, B Hospital Surveillance data. Expert Syst. Appl. 38, 11421–11436
Loong Ang, S., Choon Ong, H., Chin Low, H.: Classification using the general Bayesian network. Sci. Technol. 24 (1) 205−211(2016)
Ghosh, A.K.: On optimum choice of k in nearest neighbor classification. 50, 3113–3123 (2006)
Top Data Mining Algorithms Identified by IEEE & Related Python Resources. http://www.datasciencecentral.com/profiles/blogs/python-resources-for-top-data-mining-algorithms
Ashfaq Ahmed, K., Aljahdali, S., Hussain, S.N.: Comparative prediction performance with support vector machine and random forest classification techniques. Int. J. Comput. Appl. 69(11),12–16 (2013)
Vijayarani, S., Dhayanand, S.: Kidney disease prediction using SVM and ANN algorithms. Int. J. Comput. Business Res. 6(2), (2015)
Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. IEEE 1(8), 108–115 (2012)
Fan, Q., Zhu, C.J., Yin, L.: Predicting breast cancer recurrence using data mining techniques. IEEE 1(10), 310–311 (2010)
Lakshmi, K.R., Nagesh,Y., VeeraKrishna, M.: Performance comparison of three data mining techniques for predicting kidney dialysis survivability. Int. J. Adv. Eng. Technol. 7(1), 242–254 (2014)
Vijayarani, S., Dhayanand, S.: Data mining classification algorithms for kidney disease prediction. Int. J. Cybern. Inf. (IJCI) 4(4), 13–25 (2015)
Hall, M., Frank, E., Holmes, G., Pfahringer, B.: The WEKA data mining software: an update. 11(1), 10–18 (2009)
Chronic kideney Data Set. https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease#
Ma, H., Bandos, A.I., Rockette, H.I., Gur, D.: On use of partial area under the ROC curve for evaluation of diagnostic performance, static in medicine. Statist. Med. 32, 3449–3458 (2013)
Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. 45, 427–437 (2009)
Santos, F.: The Kappa Cohen: a tool to measure the inter-rater agreement on qualitative characters. http://www.pacea.u-bordeaux1.fr/IMG/pdf/Kappa_Cohen.pdf (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Boukenze, B., Haqiq, A., Mousannif, H. (2017). Predicting Chronic Kidney Failure Disease Using Data Mining Techniques. In: El-Azouzi, R., Menasche, D.S., Sabir, E., De Pellegrini, F., Benjillali, M. (eds) Advances in Ubiquitous Networking 2. UNet 2016. Lecture Notes in Electrical Engineering, vol 397. Springer, Singapore. https://doi.org/10.1007/978-981-10-1627-1_55
Download citation
DOI: https://doi.org/10.1007/978-981-10-1627-1_55
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1626-4
Online ISBN: 978-981-10-1627-1
eBook Packages: EngineeringEngineering (R0)