Risk Analysis of Diabetes Mellitus by Association Rule Summarization

Authors(6) :-Aishwarya Ingle, Shivani Paraskar, Avanti Ajane, Sneha Mohadikar, Lokesh Thota, Prof. Ashwini Yerlekar

Early detection of patients with elevated risk of diabetes is very important in order that patients will begin to manage diabetes early and probably stop or delay the intense disease complications. By applying association rule mining to Electronic Medical Records (EMR), we intend to discover the set of risk factors and their respective collection that betokens the patients at particularly high risk of enrooting diabetes. We studied three association rule summarization technique and did a relative evaluation of these methodologies. We made use of these methodologies to find the fundamental segments which incite high risk of diabetes. All these three strategies made summations that portrayed sub masses at a high threat of diabetes with each system having its unmistakable quality. According to our inspiration, we use bottom up summarization (BUS) which conveys more fitting summary.

Authors and Affiliations

Aishwarya Ingle
BE Scholar, Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Research, Nagpur, Maharashtra, India
Shivani Paraskar
BE Scholar, Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Research, Nagpur, Maharashtra, India
Avanti Ajane
BE Scholar, Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Research, Nagpur, Maharashtra, India
Sneha Mohadikar
BE Scholar, Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Research, Nagpur, Maharashtra, India
Lokesh Thota
BE Scholar, Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Research, Nagpur, Maharashtra, India
Prof. Ashwini Yerlekar
Assistant Professor, Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Research, Nagpur, Maharashtra, India

Diabetes Mellitus, 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. 355378, 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. 393403, 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 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
Manuscript Number : CSEIT1833317
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Aishwarya Ingle, Shivani Paraskar, Avanti Ajane, Sneha Mohadikar, Lokesh Thota, Prof. Ashwini Yerlekar, "Risk Analysis of Diabetes Mellitus by Association Rule Summarization", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp., March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833317

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