Manuscript Number : CSEIT172354
Implementing Association Rule Summarization for Predicting Relative Risk for Diabetes Mellitus
Authors(2) :-Taslim N. Kureshi, Prof. Hemlata Dakhore 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.
Taslim N. Kureshi Data mining, Association rule mining, survival analysis, association rule summarization Publication Details Published in : Volume 2 | Issue 3 | May-June 2017 Article Preview
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
Date of Publication : 2017-06-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 517-524
Manuscript Number : CSEIT172354
Publisher : Technoscience Academy
Journal URL : http://ijsrcseit.com/CSEIT172354