Implementing Association Rule Summarization for Predicting Relative Risk for Diabetes Mellitus

Authors

  • 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

Keywords:

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

Abstract

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.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-06-30

Issue

Volume 2, Issue 3, May-June-2017

Section

Research Articles

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How to Cite

[1]
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.