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Chronic Kidney Disease (CKD) Prediction Using Supervised Data Mining Techniques


Affiliations
1 PG and Research Department of Computer Science, M.G.R College, Hosur, Krishnagiri-635002, Tamilnadu, India
 

Diseases are causing high rates of mortality in the modern world, chronic kidney disease (CKD) is one of the major causes of mortality, and it has a long-term disability. The predisposing factors for CKD include diabetes mellitus, hypertension, cardiovascular diseases, smoking, obesity, family history of kidney disease and congenital kidney problems. CKD is associated with many complications such as, proteinuria, anaemia of CKD, CKD mineral and bone disorder, dyslipidemia and electrolytes imbalance. Renal replacement therapy (dialysis and kidney transplantation) is the treatment of choice for CKD. Data mining is an accurate technique helps to predict the disease using various methods includes logistic regression, naive bayes classification, k-nearest neighbours, and support vector machine. Apart from these previous techniques, it was necessary to use a classification method for data segmentation according to their diagnosis and regression method for finding risk factors. In this present study, data are classified using proposed Identification of Pattern Mining, Decision Tree methods and regression techniques are used to obtain the best levels and this can be taken as metrics that the proposed methods can help in diagnosing a patient with CKD.

Keywords

Chronic Kidney Disease, CKD, Data Mining, Identification of Pattern Mining, Decision Tree
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  • I.H. Witten, E. Frank, M.A. Hall, Data Mining: Practical Machine Learning Tools and Techniques (United States: Elsevier, 2011).

  • S. Varughese and G. Abraham, Chronic Kidney Disease in India, Clinical Journal of the American Society of Nephrology, 13(5), 2018, 802-804.

  • H. Masethe and M. Masethe, Prediction of heart disease using classification algorithms, Lecture Notes in Engineering and Computer Science, vol. 2, 2014, 809–812.

  • S. B. Patil and P. D. Scholar, Extraction of significant patterns from heart disease warehouses for heart attack, International Journal on Computer Science and Network Security, 9(2), 2009, 228–235.

  • P. K. Anooj, Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules, Journal of computer sciences, 24(1), 2012, 27- 40.

  • V. Manikandan and S. Latha, Predicting the analysis of heart disease symptoms using medicinal data mining methods, International Journal of Advanced Computer theory and Engineering, 3(2), 2013, 46- 51.

  • R. Chitra and V. Seenivasagam, Review of heart disease prediction system using data mining and hybrid intelligent techniques, ICTACT Journal on Soft Computing, 3(4), 2013, 605-609.

  • A. G. Kkaregowda, V. Punya, M. A. Jayaram and A. S. Manjunath, Rule based classification for diabetic patients using cascaded k-means and decision tree c4.5., International Journal of Computer Applications. 45(12), 2012, 45–50.

  • J. Han, J. C. Rodriguez and M. Beheshti, Diabetes data analysis and prediction model discovery using RapidMiner, Second International Conference on Future Generation Communication and Networking, 2008, 96-99. doi: 10.1109/FGCN.2008.226.

  • J. L. Breault and Breault JL. Data mining diabetic databases: Are rough sets a useful addition. In: In Proc 33rd Symposium on the Interface, Computing Science and Statistics, 2001.

  • D. Vijayalakshmi and K. Thilagavathi, An approach for prediction of diabetic disease by using bcolouring technique in clustering analysis, International Journal of Applied Mathematical Research, 1(4), 2012, 520-530.

  • T. K. Chen, D. H. Knicely and M. E. Grams, Chronic kidney disease diagnosis and management, The Journal of the American Medical Association, 322(13), 2019, 1294–1304.

  • A. S. Go, G. M. Chertow, D. Fan, C. E. McCulloch and C. Y. Hsu, Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization, The New England Journal of Medicine, 351 (13), 2004, 1296-1305.

  • R. Thomas, A. Kanso and J. R. Sedor, P. Kathuria and B. Wedro, Chronic kidney disease and its complications, Prim Care, 35(2), 2008, 329–vii.

  • M. J. Huang, M. Y. Chen and S. C. Lee, Integrating data mining with case-based reasoning for chronic diseases prognosis and diagnosis, Expert Systems with Applications, 32(3), 2007, 856–867.

  • G. Sharma and U. Hengaju, Performance Analysis of Data Mining Classification Algorithm to Predict Diabetes, International Journal of Advanced Networking and Applications, 12(1)2020, 4509- 4518.

  • R. K. Singh and M. Sivabalakrishnan, Feature selection of gene expression data for cancer classification: A review, Procedia Computer Science, 50, 2015, 52–57.

  • S. Chao-Ton and C-H. Yang, Feature selection for the SVM: An application to hypertension diagnosis, Expert Systems with Applications, 34(1), 2008, 754– 763.

  • B. Kumari and T. Swarnkar, Filter versus wrapper feature subset selection in large dimensionality micro array: A review, International Journal of Computer Science and Information Technologies, 2(3), 2011, 1048–1053.

  • O. Villacampa, Feature selection and classification methods for decision making: a comparative analysis, CEC Theses and Dissertations. College of Engineering and Computing. Nova Southeastern University, Florida, USA, 2015.

  • A. G. Karegowda, M. A. Jayaram and A. S. Manjunath, Feature subset selection problem using wrapper approach in supervised learning, International Journal of Computer Applications, 1(7), 2010, 13–17.

  • B. H. Cho, H. Yu, K. W. Kim, T. H. Kim, I. Y. Kim and S. I. Kim, Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods, Artificial Intelligence in Medicine, 42(1), 2008, 37– 53.

  • L. Ladha and T. Deepa, Feature selection methods and algorithms, International Journal of Computer Science and Engineering, 3(5), 2011, 1787–1797.

  • L. Mousin, L. Jourdan, M. E. Marmion and C. Dhaenens, Feature selection using tabu search with learning memory: learning Tabu Search, 10th International Conference. LION 10. Ischia, Italy, 2016. doi:10.1007/978-3-319-50349-3_10.


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  • Chronic Kidney Disease (CKD) Prediction Using Supervised Data Mining Techniques

Abstract Views: 196  |  PDF Views: 1

Authors

S. Rajarajeswari
PG and Research Department of Computer Science, M.G.R College, Hosur, Krishnagiri-635002, Tamilnadu, India
T. Tamilarasi
PG and Research Department of Computer Science, M.G.R College, Hosur, Krishnagiri-635002, Tamilnadu, India

Abstract


Diseases are causing high rates of mortality in the modern world, chronic kidney disease (CKD) is one of the major causes of mortality, and it has a long-term disability. The predisposing factors for CKD include diabetes mellitus, hypertension, cardiovascular diseases, smoking, obesity, family history of kidney disease and congenital kidney problems. CKD is associated with many complications such as, proteinuria, anaemia of CKD, CKD mineral and bone disorder, dyslipidemia and electrolytes imbalance. Renal replacement therapy (dialysis and kidney transplantation) is the treatment of choice for CKD. Data mining is an accurate technique helps to predict the disease using various methods includes logistic regression, naive bayes classification, k-nearest neighbours, and support vector machine. Apart from these previous techniques, it was necessary to use a classification method for data segmentation according to their diagnosis and regression method for finding risk factors. In this present study, data are classified using proposed Identification of Pattern Mining, Decision Tree methods and regression techniques are used to obtain the best levels and this can be taken as metrics that the proposed methods can help in diagnosing a patient with CKD.

Keywords


Chronic Kidney Disease, CKD, Data Mining, Identification of Pattern Mining, Decision Tree

References