Estimating the Risk of Diabetes Using Association Rule Mining Based on Clustering

Author:

Senthamarai N.,Sathya M.,Ibrahim S. Jafar Ali,Chakravarthy N. S. Kalyan

Publisher

Springer International Publishing

Reference13 articles.

1. Caraballo, P. J., Castro, M. R., Cha, S. S., Li, P. W., & Simon, G. J. (2011). Use of association rule mining to assess diabetes risk in patients with impaired fasting glucose. AMIA Annual Symposium, 3, 1705.

2. Kim, H. S., Mi Shin, A., Kyung Kim, M., & Nyun Kim, Y. (2012). Comorbidity study on type 2 diabetes mellitus using data mining. The Korean Journal of Internal Medicine, 27, 197–202.

3. Wilson, P. W., Meigs, J. B., Sullivan, L., Fox, C. S., Nathan, D. M., & Agostino, R. B. D. (2007). Prediction of incident diabetes mellitus in middle aged adults the Framingham Offspring Study. Archives of Internal Medicine, 167(10), 1068–1074.

4. Shin, A. M., Lee, I. H., & Lee, G. H. (2010). Diagnostic analysis of patients with essential hypertension using association rule mining. Korean Society of Medical Informatics, 16(2), 77–81.

5. Chandola, V., & Kumar, V. (2006). Summarization – Compressing data into an informative representation. Knowledge and Information Systems, 12(3), 355–378.

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