[
1. Chen TK, Knicely DH, Grams ME. Chronic Kidney Disease Diagnosis and Management: A Review. JAMA - Journal of the American Medical Association. 2019;322(13):1294–1304. doi: 10.1001/jama.2019.1474510.1001/jama.2019.14745
]Search in Google Scholar
[
2. Coresh J. Astor BC, Greene T, Eknoyan G, Levey AS. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. American Journal of Kidney Diseases. 2003;41(1):1–12. doi: 10.1053/ajkd.2003.50007.10.1053/ajkd.2003.50007
]Search in Google Scholar
[
3. Tuominen TK, Jämsä T, Oksanen J, Tuukkanen J, Gao TJ, Lindholm TS, Jalovaara PK. Composite implant composed of hydroxyapatite and bone morphogenetic protein in the healing of a canine ulnar defect. Annales Chirurgiae et Gynaecologiae. 2001;90(1):32-36.10.1007/s002640000208
]Search in Google Scholar
[
4. Evans PD, Taal MW. Epidemiology and causes of chronic kidney disease. Chronic Renal Failure. 2011;39(7):402–406.10.1016/j.mpmed.2011.04.007
]Search in Google Scholar
[
5. Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, et al. Chronic kidney disease: Global dimension and perspectives. The Lancet: series Global Kindey Disease. 2013;382(9888):260–272.10.1016/S0140-6736(13)60687-X
]Search in Google Scholar
[
6. Levey AS, Astor BC, Stevens LA, Coresh J. Chronic kidney disease, diabetes, and hypertension: What’s in a name. Kidney International. 2010;78(1):19–22. doi: 10.1038/ki.2010.115.10.1038/ki.2010.11520428101
]Search in Google Scholar
[
7. Kunwar V, Chandel K, Sabitha AS, Bansal A. Chronic Kidney Disease analysis using data mining classification techniques. 6th International Conference - CloudSystem and Big Data Engineering (Confluence). 2016;300–305. doi: 10.1109/CONFLUENCE.2016.7508132.10.1109/CONFLUENCE.2016.7508132
]Search in Google Scholar
[
8. Manonmani M, Balakrishnan S. Feature Selection Using Improved Teaching Learning Based Algorithm on Chronic Kidney Disease Dataset. Procedia Computer Science. 2020;171(2019):1660–1669. doi: 10.1016/j.procs.2020.04.17810.1016/j.procs.2020.04.178
]Search in Google Scholar
[
9. Dardzińska A. Action Rules Mining. Springer-Verlag, Berlin. 2013.10.1007/978-3-642-35650-6
]Search in Google Scholar
[
10. Avci E, Karakus S, Ozmen O, Avci D. Performance comparison of some classifiers on Chronic Kidney Disease data. 6th International Symposium on Digital Forensic and Security (ISDFS). 2018;1-4. doi: 10.1109/ISDFS.2018.8355392.10.1109/ISDFS.2018.8355392
]Search in Google Scholar
[
11. Rady EHA, Anwar AS. Prediction of kidney disease stages using data mining algorithms. Informatics in Medicine Unlocked. 2019;15:100178. doi: 10.1016/j.imu.2019.100178.10.1016/j.imu.2019.100178
]Search in Google Scholar
[
12. Akben SB. Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis, Blood Analysis and Disease History. IRBM. 2018;39(5):353–358. doi: 10.1016/j.irbm.2018.09.004.10.1016/j.irbm.2018.09.004
]Search in Google Scholar
[
13. Simunovic VL. Basic & General Clinical Skills. CreateSpace Independent Publishing Platform. 2013.
]Search in Google Scholar
[
14. Freeth A. Diabetes Causes, Myths, Treatment, and Home Care. eMediHealth. 2019.
]Search in Google Scholar
[
15. Jujo K, Minami Y, Haruki S, Matsue Y, Shimazaki K, Kadowaki H, Ishida I, Kambayashi K, Arashi H, Sekiguchi H, Hagiwara N. Persistent high blood urea nitrogen level is associated with increased risk of cardiovaserum creatinineular events in patients with acute heart failure. ESC Heart Failure, 2017;4(4):545–553.10.1002/ehf2.12188569517729154415
]Search in Google Scholar
[
16. Piñol-Ripoll G, De La Puerta I, Purroy F. Serum creatinine is an inadequate screening test for renal failure in ischemic stroke patients. Acta Neurologica Scandinavica. 2009;120(1):47–52. doi: 10.1111/j.1600-0404.2008.01120.x.10.1111/j.1600-0404.2008.01120.x19486327
]Search in Google Scholar
[
17. Strazzullo P, Leclercq C. Nutriente information: Sodium. Advances in Nutrition. 2014;5(2):188–90 doi: 10.3945/an.113.005215.10.3945/an.113.005215395180024618759
]Search in Google Scholar
[
18. Kardalas E, Paschou SA, Anagnostis P, Muscogiuri G, Siasos G, Vryonidou A. Hypokalemia: A clinical update. Endocrine Connections. 2018;7(4):135–146. doi: 10.1530/EC-18-0109.10.1530/EC-18-0109588143529540487
]Search in Google Scholar
[
19. Walker HK, Hall WD HJ. Clinical Methods: The History, Physical, and Laboratory Examinations. 3rd edition. 1990.
]Search in Google Scholar
[
20. Fairbanks VF, Tefferi A. Normal ranges for packed cell volume and hemoglobin concentration in adults: Relevance to “apparent polycythemia.” European Journal of Haematology. 2000;65(5): 285–296. doi: 10.1034/j.1600-0609.2000.065005285.x.10.1034/j.1600-0609.2000.065005285.x11092458
]Search in Google Scholar
[
21. White Blood Cell Count. Nursing Critical Care. 2019;14:1-40. doi: 10.1097/01.CCN.0000549633.67301.6d10.1097/01.CCN.0000549633.67301.6d
]Search in Google Scholar
[
22. Red Blood Cell Count. Nursing Critical Care. 2020;15(1):1-38. doi: 10.1097/01.CCN.0000612852.86589.d210.1097/01.CCN.0000612852.86589.d2
]Search in Google Scholar
[
23. Hall MA. Correlation-based Feature Selection for Machine Learning. Doctoral thesis. University of Waikato. 1999.
]Search in Google Scholar
[
24. Sun J, Zhang X, Liao D, Chang V. Efficient method for feature selection in text classification. 2017 International Conference on Engineering and Technology (ICET). 2017;1–6. doi: 10.1109/ICEngTechnol.2017.8308201.10.1109/ICEngTechnol.2017.8308201
]Search in Google Scholar
[
25. An TK, Kim MH. A new Diverse AdaBoost classifier. Artificial Intelligence and Computational Intelligence. 2010;1:359–363. doi: 10.1109/AICI.2010.82.10.1109/AICI.2010.82
]Search in Google Scholar
[
26. Kegl B, Introduction to AdaBoost. 2014. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.679.8866&rep=rep1&type=pdf, 10 October 2021.
]Search in Google Scholar
[
27. Zeng ZQ, Yu H Bin, Xu HR, Xie YQ, Gao J. Fast training Support Vector Machines using parallel Sequential Minimal Optimization. rd International Conference on Intelligent System and Knowledge Engineering. 2008;997–1001. doi: 10.1109/ISKE.2008.4731075.10.1109/ISKE.2008.4731075
]Search in Google Scholar
[
28. Abirami S, Chitra P. Energy-efficient edge based real-time healthcare support system. Advances in Computers. 2020;117(1):339–368. doi: 10.1016/bs.adcom.2019.09.00710.1016/bs.adcom.2019.09.007
]Search in Google Scholar
[
29. Kumar Y, Sahoo G. Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA. International Journal of Information Technology and Computer Science 2012; 4(7):43–9. doi: 10.5815/ijitcs.2012.07.06.10.5815/ijitcs.2012.07.06
]Search in Google Scholar
[
30. Humphris CW. Computer Science Principles V10. CreateSpace Independent Publishing Platform. 2013.
]Search in Google Scholar
[
31. Saravana N, Gayathri V. Performance and Classification Evaluation of J48 Algorithm and Kendall’s Based J48 Algorithm (KNJ48). International Journal of Computer Trends and Technology. 2018;59(2):73–80. doi: 10.14445/22312803/ijctt-v59p112.10.14445/22312803/IJCTT-V59P112
]Search in Google Scholar
[
32. Waseem S, Salman A, Muhammad AK. Feature subset selection using association rule mining and JRip classifier. International Journal of Physical Sciences. 2013;8(18):885–96. doi: 10.5897/ijps2013.3842.10.5897/IJPS2013.3842
]Search in Google Scholar
[
33. Lewis RJ, Ph D, Street WC. An Introduction to Classification and Regression Tree (CART) Analysis. 2000. Available from: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.95.4103&rep=rep1&type=pdf, 10 October 2021.
]Search in Google Scholar
[
34. Frank E, Witten IH. Generating accurate rule sets without global optimization. Hamilton, New Zealand: University of Waikato, Department of Computer Science. 1998.
]Search in Google Scholar
[
35. Kalmegh S. Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News. International Journal of Innovative Science, Engineering & Technology. 2015;2(2):438–446.
]Search in Google Scholar
[
36. Bro R, Kjeldahl K, Smilde AK, Kiers HAL. Cross-validation of component models: A critical look at current methods. Analytical and Bioanalytical Chemistry. 2008;390(5):1241–1251. doi: 10.1007/s00216-007-1790-1.10.1007/s00216-007-1790-118214448
]Search in Google Scholar
[
37. Kohavi R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. Morgan Kaufmann. 1995.
]Search in Google Scholar
[
38. Novakovic J, Veljovi A, Iiic S, Papic Z, Tomovic M. Evaluation of Classification Models in Machine Learning. Theory and Applications of Mathematics & Computer Science. 2017;7(1):39–46.
]Search in Google Scholar
[
39. Aggarwal CC. [ed.] Data Classification - Algorithms and Applications, Chapman and Hall/CRC. 2014.
]Search in Google Scholar
[
40. Maimon O, Rokach L. [ed.] Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers. Berlin, Springer. 2005.10.1007/b107408
]Search in Google Scholar
[
41. Ras ZW, Dardzinska A. Action Rules Discovery Based on Tree Classifiers and Meta-actions. Lecture Notes in Artificial Intelligence. 2009;5722;66–75.10.1007/978-3-642-04125-9_10
]Search in Google Scholar
[
42. Ras ZW, Dardzinska A. Action Rules Discovery without Pre-existing Classification Rules. Lecture Notes in Computer Science, 2008; 5306:181-19010.1007/978-3-540-88425-5_19
]Search in Google Scholar
[
43. Jongbo OA. Adetunmb AO, Ogunrinde RB, Badeji-Ajisafe B. Development of an ensemble approach to chronic kidney disease diagnosis. Scientific African, 2020;8:e00456. doi: 10.1016/j.sciaf.2020.e0045610.1016/j.sciaf.2020.e00456
]Search in Google Scholar
[
44. Senan EM, Al-Adhaileh MH, Alsaade FW, et al. Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques. Journal of Healthcare Engineering. 2021;2021:1004767. doi: 10.1155/2021/1004767.10.1155/2021/1004767820884334211680
]Search in Google Scholar