Review on Data Mining Techniques for Prediction of Chronic Kidney Disease

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2018 by IJETT Journal
Volume-63 Number-1
Year of Publication : 2018
Authors : Pallavi Sharma, Gurmanik Kaur
DOI :  10.14445/22315381/IJETT-V63P209


MLA Style: Pallavi Sharma, Gurmanik Kaur"Review on Data Mining Techniques for Prediction of Chronic Kidney Disease" International Journal of Engineering Trends and Technology 63.1 (2018): 58-60.

APA Style:Pallavi Sharma, Gurmanik Kaur (2018). Review on Data Mining Techniques for Prediction of Chronic Kidney Disease. International Journal of Engineering Trends and Technology, 63(1), 58-60.

Data mining is the process of extracting hidden interesting patterns from massive database. Medical domain contains heterogeneous data that can be mined properly to provide a variety of useful information for the physicians to detect a disease, predict the survivability of the patients after disease, severity of diseases etc. The main aim of this paper is to analyse the application of data mining in medical domain for prediction of chronic kidney disease. In the healthcare area chronic kidney disease can be very well predicted using data mining techniques.

[1] Neelamadhab Padhy, Dr. Pragnyaban Mishra and Rasmita Panigrahi (2012), “The Survey of Data Mining Applications and Feature Scope”, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 2(3), pp 43-58.
[2] J.C.Prather, D. F. Lobach, L. K. Goodwin, J. W. Hales, M. L. Hage, and W. E. Hammond (1997), “Medical data mining: knowledge discovery in a clinical data warehouse”, Proceedings of the AMIA Annual Fall Symposium, pp 101–105.
[3] Neesha Jothi, Nur Aini Abdul Rashid, Wahidah Husain (2015), “Data mining in Healthcare: A Review”, Procedia Computer Science, 72, pp 306-313.
[4] Subhash Chandra Pandey (2016), “Data mining techniques for medical data: A review”, International Conference on Signal Processing, Communication, Power and Embedded System, Paralakhemundi, India, pp 972-982.
[5] Levey A. S. and Coresh, J. (2012), “Chronic kidney disease”, Lancet, 379, pp 165–180.
[6] Wang H, Naghavi M, Allen C (2016), “GBD 2015 Mortality and Causes of Death Collaborators. Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015”, Lancet, 388, pp 1459–544.
[7] Sirage Zeynu and Shruti Patil (2018), “Prediction of Chronic Kidney Disease using Data Mining Feature Selection and Ensemble Method”, International Journal of Data Mining in Genomics & Proteomics, 9(1), pp 1-9
[8] Rajni Garg and Vikas Mongia (2018), “A Comparative Study of Different Classification Algorithms on Kidney Disease Prediction”, International Journal for Research in Applied Science & Engineering Technology, 6(2), pp 741-746.
[9] S.Gopika and Dr.M.Vanitha (2017), “Efficiency of Data Mining Techniques for Predicting Kidney Disease”, International Journal of Engineering and Technology, 9(5), pp 3586-3591
[10] Basma Boukenze, Abdelkrim Haqiq and Hajar Mousannif (2017), “Predicting Chronic Kidney Failure Disease using Data Mining Techniques”, Advances in Ubiquitous Networking, Springer, pp 701-712.
[11] Pavithra N and Dr. R. Shanmugavadivu (2017), “Efficient Early Risk Factor Analysis of Kidney Disorder using Data mining Technique”, International Journal of Innovative Research in Computer and Communication Engineering, 5(2), pp 1690-1698.
[12] Veenita Kunwar, Khushboo Chandel, A.Sai Sabitha and Abhay Bansal (2016), “Chronic Kidney Disease Analysis Using Data Mining Classification Techniques”, 6th International Conference on Cloud System and Big Data Engineering, pp 300-305
[13] Basma Boukenze, Hajar Mousannif and Abdelkrim Haqiq (2016), “Performance of Data Mining Techniques to Predict in Healthcare Case Study: Chronic Kidney Failure Disease”, International Journal of Database Management Systems, 8(3), pp 1-9.
[14] Neha Sharma and Rohit Kumar Verma (2016), “Prediction of Kidney Disease by using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, 6(9), pp 66-70
[15] Veenita Kunwar, Khushboo Chandel, A. Sai Sabitha, and Abhay Bansal (2016), “Chronic Kidney Disease Analysis using Data Mining Classification Techniques”, IEEE on Cloud System and big data Engineering , pp 300-305.
[16] Parul Sinha and Poonam Sinha (2015), “Comparative study of Chronic kidney disease prediction using KNN and SVM”, International Journal of Engineering Research & Technology, 4(12), pp 608-612
[17] Swathi Baby P and Panduranga Vital T (2015), “Statistical Analysis and Predicting Kidney Diseases using Machine Learning Algorithms”, International Journal of Engineering Research & Technology, 4(7), pp 206-210.
[18] Vijayarani, S. and Dhayanand, S. (2015), “Data mining classification algorithms for kidney disease prediction”, Int. J. Cybern. Inf. (IJCI), 4(4), pp 13–25.
[19] Lambodar Jena and Narendra K. Kamila (2015), “Distributed Data Mining Classification Algorithms for Prediction of Chronic- Kidney-Disease”, International Journal of Emerging Research in Management & Technology, 4, (11), pp 110-118
[20] Vijayarani, S., Dhayanand, S. (2015), “Kidney disease prediction using SVM and ANN algorithms”, International Journal of Computing and Business Research, 6(2).
[21] Shakil Ahmed, Md. Tanzir Kabir, Navid Tanzeem Mahmood and Rashedur M Rahman (2014), “Diagnosis of kidney disease using fuzzy expert system”, IEEE conference on software, knowledge, Information management and Applications.
[22] Tommaso Di Noia, Vito Claudio Ostuni, Francesco Pesce, Giulio Binetti, David Naso, Francesco Paolo Schena and Eugenio Di Sciascio (2013), “An end stage kidney disease predictor based on an artificial neural networks ensemble”, Elsevier Publication, Expert Systems with Applications , 4438-4445.
[23] Lakshmi, K.R., Nagesh, Y., Veera Krishna, M. (2013), “Performance comparison of three data mining techniques for predicting kidney dialysis survivability”, International Journal of Advances in Engineering & Technology, 7(1), 242–254.
[24] Ruey Kei Chiu and Renee Yu-Jing (2011),” Constructing Models for Chronic Kidney Disease Detection and Risk Estimation”, Proceedings of 22nd IEEE International Symposium on Intelligent Control, Singapore, 166-171.
[25] Julia Hippisley-Cox* and Carol Coupland (2010), “Predicting the risk of Chronic Kidney Disease in Men and Women in England and Wales: prospective derivation and external validation of the Qkidney scores”, BMC family practice, 1-13.
[26] Susan Snyder, M.D. and Bernadette Pendergraph, M.D. (2005), "Detection and Evaluation of Chronic Kidney Disease", American Family Physician, 72(9), 1723-1732.

Data mining, medical data, chronic kidney disease, disease prediction.