The main aim of this project is to predict the excess risk of diabetes for the patients and summarize their sub population by using Association Rule Mining. In Data Mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. To Apply Association Rule Mining to electronic medical records (EMR) to discover sets of risk factors and their corresponding subpopulations that represent patients at particularly high risk of developing diabetes. An Electronic Medical Record (EMR) is an evolving concept defined as a systematic collection of electronic health information about individual patients or population. The high dimensional of EMR’s, association rule mining generates a very large set of rules which we need to summarize for easy clinical use. Applied four association rule set stigmatization techniques and conducted a comparative evaluation to provide guidance regarding their applicability, strengths and weaknesses. We found that all four methods produced summaries that described sub populations at high risk of diabetes with each method having its clear strength. For our purpose, our extension to the Bottom-Up Stigmatization (BuS) is the best practice in the entire above summary.
B. Murugeshwari, Jannathul Firdous A, Venmathi V
Data Mining, Associationrule Mining, Survival Analysis, Summarization Technique
- F. Afrati, A. Gionis, and H. Mannila, "Approximating a collection of frequent sets," in Proc. ACM Int. Conf. KDD, Washington, DC, USA, 2004.
- R. Agrawal and R. Srikant, "Fast algorithms for mining association rules," in Proc. 20th VLDB, Santiago, Chile, 1994.
- Y. Aumann and Y. Lindell, "A statistical theory for quantitative association rules," in Proc. 5th KDD, New York, NY, USA, 1999.
- 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.
- 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 .
- V. Chandola and V. Kumar, "Summarization – Compressing data into an informative representation," Knowl. Inform. Syst., vol. 12, no. 3, pp. 355–378, 2006.
- 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.
- 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.
|Published in :
||Volume 2 | Issue 2 | March-April - 2016
|Date of Publication
Cite This Article
B. Murugeshwari, Jannathul Firdous A, Venmathi V, "Extending Association Rule Summarization Techniques to Assess Risk of Diabetes Mellitus", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.466-468, March-April-2016.
URL : http://ijsrset.com/IJSRSET1622151.php