A Survey of Predicting Relative Risk for Diabetes Mellitus Using Association Rule Summarization Techniques |
Author(s): |
| Sinduja.K , KSR COLLEGE OF ENGINEERING; N.Saravanan, KSR COLLEGE OF ENGINEERING |
Keywords: |
| Data Mining, Association Rule Summarization, Survival Analysis, Association Rules, Regression, Top-K, Markov Random Field |
Abstract |
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The detection of diabetes mellitus with elevated risk at early stage is critical in global clinical management. It aims to apply association rule mining to electronic medical records (EMR) to detect sets of risk factors and their corresponding subpopulations of patients. Association rule mining accomplishes a very large set of rules for summarizing the risk of diabetes in EMR with high dimensionality. To review the association rule set summarization techniques and conducted comparative evaluation to provide the best optimal summary based on their merits and demerits. In this paper, discuss about various methods to summarize the high risk of diabetes with accuracy. |
Other Details |
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Paper ID: IJSRDV3I80083 Published in: Volume : 3, Issue : 8 Publication Date: 01/11/2015 Page(s): 106-107 |
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