N. Kanagavalli, A .Vijayaraj, B. Sakthi Saravanan, Saveetha Engineering College, Chennai, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.07.05
International Journal of Advanced Information and Communication Technology
Received On : June 08, 2016
Revised On : July 18, 2016
Accepted On : August 10, 2016
Published On : September 05, 2016
Volume 03, Issue 09
Pages : 561-563
Abstract
The World Health Organization estimates that by 2030 there will be approximately 450 million people with diabetes, associated with renal complications, heart disease, stroke and peripheral vascular disease. Our aim is to analyses the risk factors and co morbidity conditions to detect diabetes early. Using PubMed and EMBASE databases to identify and extract key information that describes aspects of developing a prediction model, sample size and number of events, risk predictor selection. Using these two methods, we identify the various attributes and assign a value to each and every attribute or parameter. Based on the parameters, the analyses of high risk factors of developing diabetes are identified using association rule mining..
Keywords
Risk Factors, Comorbidity, Searching Methods, Diabetes, Insert.
Cite this article
N. Kanagavalli, A .Vijayaraj, B. Sakthi Saravanan, “Evaluation on Risk Factors and Comorbidity Conditions of Diabetes using Mining Algorithms and Searching Methods” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.561-563, September 05, 2016.
Copyright
© 2016 N. Kanagavalli, A .Vijayaraj, B. Sakthi Saravanan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.