Manuscript Number : CSEIT172354

Implementing Association Rule Summarization for Predicting Relative Risk for Diabetes Mellitus

Authors(2) :-Taslim N. Kureshi, Prof. Hemlata Dakhore

Diabetes is a developing pandemic of non-transmittable malady which influences the greater part of the general population on the planet. Keeping in mind the end goal to stifle the development of diabetes mellitus we utilize affiliation control rundown to electronic medicinal records to find set of hazard variables and the comparing sub-populace which speaks to patients at especially high danger of creating diabetes. Typically affiliation control mining creates huge volume of informational collections which we have to outline for any therapeutic record or any clinical utilize. We join four strategies to locate the basic components which prompt high danger of diabetes all these four techniques created synopses that depicted sub populaces at high danger of diabetes with every strategy having its unmistakable quality. As per our motivation we utilize bottom up summarization (BUS) calculation which delivers more appropriate rundown.

Authors and Affiliations

Taslim N. Kureshi
Department of Computer Science and Engineering, G. H. Raisoni institute of Technology and Engineering, Nagpur, Maharashtra, India
Prof. Hemlata Dakhore
Department of Computer Science and Engineering, G. H. Raisoni institute of Technology and Engineering, Nagpur, Maharashtra, India

Data mining, Association rule mining, survival analysis, association rule summarization

  1. F. Afrati, A. Gionis, and H. Mannila, “Approximating a collection of frequent sets,” in Proc. ACM Int. Conf. KDD, Washington, DC, USA, 2004.
  2. R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. 20th VLDB, Santiago, Chile, 1994.
  3. Y. Aumann and Y. Lindell, “A statistical theory for quantitative association rules,” in Proc. 5th KDD, New York, NY, USA, 1999.
  4. P. J. Caraballo, M. R. Castro, S. S. Cha, P. W. Li, and G. J. Simon, “Use of association rule mining to assess diabetes risk in patients with impared fasting glucose,” in Proc. AMIA Annu. Symp., 2011.
  5. Centers for Disease Control and Prevention. “National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States,” U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, 2011 [Online].
  6. V. Chandola and V. Kumar, “Summarization – Compressing data into an informative representation,” Knowl. Inform. Syst., vol. 12, no. 3, pp. 355–378, 2006.
  7. G. S Collins, S. Mallett, O. Omar, and L.-M. Yu, “Developing risk prediction models for type 2 diabetes: A systematic review of methodology and reporting,” BMC Med., 9:103, Sept. 2011.
  8. Diabetes Prevention Program Research Group, “Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin,” N. Engl. J. Med., vol. 346, no. 6, pp. 393–403, Feb. 2002.
  9. G. Fang et al., “High-order SNP combinations associated with complex diseases: Efficient discovery, statistical power and functional interactions,” PLoS ONE, vol. 7, no. 4, Article e33531, 2012.
  10. M. A. Hasan, “Summarization in pattern mining,” in Encyclopedia of Data Warehousing and Mining, 2nd ed. Hershey, PA, USA: Information Science Reference, 2008.
  11. Rakesh Motka, Viral Parmar, Balbindra Kumar, A. R. Verma, “ Diabetes Mellitus Forecast Using Different Data Mining Techniques”, International conference on computer and Communication Technology
  12. Prof.Sumathy, Prof.Mythili, Dr.Praveen Kumar, Jishnujit T M, K Ranjith Kumar, “Diagnosis of Diabetes Mellitus based on Risk Factors”, International Journal of Computer Applications, Vol.10, Issue No.4, November.2010
  13. Anand A. Chaudhari, Prof.S.P.Akarte, “ Fuzzy and Data Mining based Disease Predection using K-NN Algorithm”, International Journal of Innovations in Engineering and Technology, Vol. 3, Issue No. 4, April 2014
  14. Prof.Sumathy, Prof.Mythili, Dr.Praveen Kumar, Jishnujit T M, K Ranjith Kumar, “ Diagnosis of Diabetes Mellitus based on Risk Factors”, International Journal of Computer Applications, Vol. 10, Issue No. 4, November 2010
  15. Aqueel Ahmed, Shaikh Abdul Hannan, “ Data Mining Techniques to Find Out Heart Diseases: An Overview”, International Journal of Innovative Technology and Exploring Engineering, Vol. 1, Issue No. 4, September 2012
  16. P. Thangaraju, B.Deepa, T.Karthikeyan, “Comparison of Data mining Techniques for Forecasting Diabetes Mellitus”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, Issue No. 8, August 2014
  17. M. Durairaj, G. Kalaiselvi, “ Prediction Of Diabetes Using Soft Computing Techniques- A Survey”, International Journal of Scientific & Technology Research, Vol. 4, Issue No.3, March 2015
  18. S.F.B, Jaafar and Darmawaty Mohd Ali. “Diabetes Mellitus Forecast using Artificial Neural Network (ANN), Asian conference on sensors and the international conference on new techniques in pharmaceutical and medical research proceedings (IEEE), Kuala Lumpur, Malaysia, 5-7 September 2005, pp 135-139.
  19. S. Alby, B. L. Shivakumar,” A survey on data-mining technologies for prediction and diagnosis of diabetes”, International conference of IEEE 2014.
  20. Gyorgy J. Simon, Terry M. Therneau, Steven S. Cha, “ Extending association rule summarization techniques to assess risk of diabetes mellitus”, IEEE VOL 27, no. 1, January 2015

Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 517-524
Manuscript Number : CSEIT172354
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Taslim N. Kureshi, Prof. Hemlata Dakhore , "Implementing Association Rule Summarization for Predicting Relative Risk for Diabetes Mellitus", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.517-524, May-June-2017.
Journal URL : http://ijsrcseit.com/CSEIT172354

Article Preview