Data Mining Techniques Used To Predict Chronic Kidney Disease

Authors

  • Chithra A G  Department of Computer Science & Engineering, ATME College of Engineering, Mysuru, Karnataka, India
  • Chandana B  Department of Computer Science & Engineering, ATME College of Engineering, Mysuru, Karnataka, India
  • Darshan R  Department of Computer Science & Engineering, ATME College of Engineering, Mysuru, Karnataka, India
  • Harshitha H S  Department of Computer Science & Engineering, ATME College of Engineering, Mysuru, Karnataka, India
  • Nasreen Fathima  Department of Computer Science & Engineering, ATME College of Engineering, Mysuru, Karnataka, India

Keywords:

Data mining, Classification, Chronic Kidney disease, Random forest, RBF, K-means clustering.

Abstract

Chronic kidney disease is a global health issue and area of concern, associated with an increased risk of cardio vascular diseases and chronic renal failure[1]. It is a symptom where kidney fails to filter toxic wastes from the body, which results in decomposition of wastes in human body and leads to dangerous results. The two main causes of this disease are diabetes and high blood pressure, which are responsible for up to two-third of cause[5]. The healthcare sector has huge medical data but the main difficulty is how to cultivate the existing information into useful practices[3]. To unfold this hurdle the concept of data mining is best suited. The main objective of this paper is to use data mining technique such as random forest, RBF, K-means clustering and Naïve Bayes for the prediction of chronic kidney disease and to summarize the efficiency of Naïve Bayes method by generating suitable results.

References

  1. Vijayarani, S., & Dhayanand, S., 2015. Data mining classification algorithms for kidney disease prediction. International Journal on Cybernetics and Informatics (IJCI).
  2. Kumar, K., & Abhishek, B., 2012. Artificial neural networks for diagnosis of kidney stones disease.
  3. Abhishek, G. S. M. T., & Gupta, D., 2012. Proposing Efficient Neural Network Training Model for Kidney Stone Diagnosis. International Journal of Computer Science and Information Technologies, 3(3), pp. 3900-3904.
  4. Durairaj M, Ranjani V, Data mining applications in healthcare sector a study. Int. J. Sci. Technol. Res. IJSTR, 2(10), 2013.
  5. Sai Sabitha, Abhay Bansal, Khushboo Chandel, VeenitaKunwar, Chronic Kidney Disease Analysis Using DataMining Classification Techniques, 6th InternationalConference on Cloud System and Big Data Engineering,2016
  6. David S. K., Saeb A. T., Al Rubeaan K., Comparative Analysis of Data Mining Tools and Classification Techniques using WEKA in Medical Bioinformatics, Computer Engineering and Intelligent Systems, 4(13):28-38,2013.
  7. Durairaj M, Ranjani V, Data mining applications in healthcare sector a study. Int. J. Sci. Technol. Res. IJSTR, 2(10), 2013.
  8. Yasodha P, Kannan M, Analysis of Population of Diabetic Patient Database in WEKA Tool, International Journal of Science and Engineering Research, 2 (5), May 2011.
  9. Duraiaj M, Ranjani V, Data Mining applications in healthcare sector a study. Int . J. Sci. Technol. Res. IJSTR, 2(10), 2013.
  10. Hall M, Reutemann P, WEKA Knowledge Flow Tutorial for version 3-5-8, July 2008.
  11. Scuse D, Reutemann P, WEKA Experimenter Tutorial for version 3-5-5, January 2007.
  12. Shomona Gracia Jacob, R.Geetha Ramani, Discovery of Knowledge Patterns in Clinical Data through Data Mining Algorithms: Multiclass Categorization of Breast Tissue Data, International Journal of Computer Applications (0975– 8887)Volume 32– No.7, October 2011.
  13. Mihaila C, Ananiadou S, Recognising Discourse Causality Triggers in the Biomedical Domain, Journal of Bioinformatics and Computational Biology, 11(06), October 2013.
  14. Thitiprayoonwongse D., Suriyaphol P., Soonthornphisaj N., Data mining of dengue infection using decision tree, Entropy, 2: 2, 2012.
  15. Alam M., Shakil K.A., Cloud Database Management System Architecture, UACEE International Journal of Computer Science and its Applications, 3(1):27-31, 2013.
  16. Alam M., Shakil K.A., A decision matrix and monitoring based framework for infrastructure performance enhancement in a cloud based environment, International Conference on Recent Trends in Communication and Computer Networks, Elsevier, pp. 174-180, November 2013.
  17. Alam M., Shakil K.A., An NBDMMM Algorithm Based Framework for Allocation of Resources in Cloud, arXiv preprint arXiv: 1412.8028, 2014.
  18. Shakil K.A. and Alam M., Data Management in Cloud Based Environment using k-Median Clustering Technique, IJCA Proceedings on 4th International IT Summit Confluence 2013 - The Next Generation Information Technology Summit Confluence 2013, pp. 8-13, January 2014.
  19. Alam M., Shakil K.A., and Sethi S. ,Analysis and Clustering of Workload in Google Cluster Trace based on Resource Usage, arXiv preprint arXiv: arXiv: 1501.01426, 2014.
  20. Shakil K.A., Sethi S., Alam M., An Effective Framework for Managing University Data using a Cloud based Environment, arXiv preprint arXiv:1501.07056, 2015
  21. Zareen F.J. and Jabin S., A Comparative Study of recent trends in biometric signature verification,IC3, IEE, 354-358, 2013.
  22. Witten H, Ian H. 2011. Data mining: practical machine learning tools and techniques. Morgan Kaufmann Series in Data Management Systems.
  23. Tom Fawcett, (2003). ROC graphs: Notes and practical considerations for data mining researchers. Technical report, HP Laboratories.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-05-08

Issue

Volume 4, Issue 6, May-June-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Chithra A G, Chandana B, Darshan R, Harshitha H S, Nasreen Fathima, " Data Mining Techniques Used To Predict Chronic Kidney Disease, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 6, pp.54-58, May-June-2018.