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Reseach Article

Improved J48 Classification Algorithm for the Prediction of Diabetes

by Gaganjot Kaur, Amit Chhabra
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 22
Year of Publication: 2014
Authors: Gaganjot Kaur, Amit Chhabra

Gaganjot Kaur, Amit Chhabra . Improved J48 Classification Algorithm for the Prediction of Diabetes. International Journal of Computer Applications. 98, 22 ( July 2014), 13-17. DOI=10.5120/17314-7433

@article{ 10.5120/17314-7433,
author = { Gaganjot Kaur, Amit Chhabra },
title = { Improved J48 Classification Algorithm for the Prediction of Diabetes },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 22 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 13-17 },
numpages = {9},
url = { },
doi = { 10.5120/17314-7433 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:26:52.262503+05:30
%A Gaganjot Kaur
%A Amit Chhabra
%T Improved J48 Classification Algorithm for the Prediction of Diabetes
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 22
%P 13-17
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

This research work deals with efficient data mining procedure for predicting the diabetes from medical records of patients. Diabetes is a very common disease these days in all populations and in all age groups. Diabetes contributes to heart disease, increases the risks of developing kidney disease, nerve damage, blood vessel damage and blindness. So mining the diabetes data in efficient manner is a critical issue. The Pima Indians Diabetes Data Set is used in this paper; which collects the information of patients with and without having diabetes. The modified J48 classifier is used to increase the accuracy rate of the data mining procedure. The data mining tool WEKA has been used as an API of MATLAB for generating the J-48 classifiers. Experimental results showed a significant improvement over the existing J-48 algorithm.

  1. Al Jarullah, A. A. , "Decision tree discovery for the diagnosis of type II diabetes," Innovations in Information Technology (IIT), 2011 International Conference on , vol. , no. , pp. 303,307, 25-27 April 2011
  2. Folorunsho, Olaiya. "Comparative Study of Different Data Mining Techniques Performance in knowledge Discovery from Medical Database. " International Journal 3, no. 3 (2013).
  3. Huang, Feixiang; Wang, Shengyong; Chan, Chien-Chung, "Predicting disease by using data mining based on healthcare information system," Granular Computing (GrC), 2012 IEEE International Conference on , vol. , no. , pp. 191,194, 11-13 Aug. 2012
  4. Marcano-Cedeno, Alexis; Andina, Diego, "Data mining for the diagnosis of type 2 diabetes," World Automation Congress (WAC), 2012 , vol. , no. , pp. 1,6, 24-28 June 2012.
  5. Nadali, A; Kakhky, E. N. ; Nosratabadi, H. E. , "Evaluating the success level of data mining projects based on CRISP-DM methodology by a Fuzzy expert system," Electronics Computer Technology (ICECT), 2011 3rd International Conference on , vol. 6, no. , pp. 161,165, 8-10 April 2011
  6. Nincevic, I. ; Cukusic, M. ; Garaca, Z. , "Mining demographic data with decision trees," MIPRO, 2010 Proceedings of the 33rd International Convention , vol. , no. , pp. 1288,1293, 24-28 May 2010
  7. Robu, R. ; Hora, C. , "Medical data mining with extended WEKA," Intelligent Engineering Systems (INES), 2012 IEEE 16th International Conference on , vol. , no. , pp. 347,350, 13-15 June 2012
  8. Salama, G. I. ; Abdelhalim, M. B. ; Zeid, M. A. , "Experimental comparison of classifiers for breast cancer diagnosis," Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on , vol. , no. , pp. 180,185, 27-29 Nov. ,2012.
  9. S. Moertini Veronica ,"Towards The Use Of C4. 5 Algorithm For Classifying Banking Dataset",Integeral Vol 8 No 2,October 2013.
  10. UM, Ashwinkumar, and Anandakumar KR. "Predicting Early Detection of Cardiac and Diabetes Symptoms using Data Mining Techniques. ",IEEE,pp:161-165,2011
  11. Geetha Ramani R, Lakshmi Balasubramanian, and Shomona Gracia Jacob. "Automatic prediction of Diabetic Retinopathy and Glaucoma through retinal image analysis and data mining techniques. " In Machine Vision and Image Processing (MVIP), 2012 International Conference on, pp. 149-152. IEEE, 2012
  12. Sugimoto, Masahiro, Masahiro Takada and Masakazu Toi. "Comparison of robustness against missing values of alternative decision tree and multiple logistic regression for predicting clinical data in primary breast cancer. " In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp. 3054-3057. IEEE, 2013.
  13. Hussein Asmaa S,Wail M. Omar, Xue Li, and Modafar Ati. "Efficient Chronic Disease Diagnosis prediction and recommendation system. " In Biomedical Engineering and Sciences (IECBES), 2012 IEEE EMBS Conference on, pp. 209-214. IEEE, 2012.
  14. Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository [http://archive. ics. uci. edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
  15. Korting, Thales Sehn. "C4. 5 algorithm and Multivariate Decision Trees. " Image Processing Division, National Institute for Space Research--INPE.
  16. Guo, Yang, Guohua Bai, and Yan Hu. "Using Bayes Network for Prediction of Type-2 Diabetes. " In Internet Technology And Secured Transactions, 2012 International Conferece For, pp. 471-472. IEEE, 2012.
Index Terms

Computer Science
Information Sciences


J48 Decision Tree MATLAB Data Mining Diabetes WEKA.