CLINICAL DATA MINING: AN OVERVIEW OF DATA MINING TECHNIQUES IN DIABETES

Provide a health care service to the patients with diabetes provides useful information that could be used to identify, treatment, following up and prevention of diabetes. Explore and investigation of large volumes of data requires effective and efficient methods for finding hiding patterns in the data. The use of various techniques of data mining in particular Classification and Frequent patterns can be helpful.
This article is a narrative review. We searched keywords related to application of data mining in the field of diabetes, through related databases, in English language articles published from 2005 to 2015. Also related articles in the selected articles list have been analyzed.
From the 2144 articles obtained in the initial search, 38 articles related to the subject of study, were selected. Several studies shown that classification and clustering algorithms, association rules and artificial intelligence are the most widely used data mining techniques for predict the risk of diabetes has been successfully used.
The important step in control of diabetes, use of the methods that could determine the possibility or lack of diabetes. According to studies conducted in this area seem to use data mining techniques to prevent, treat and discover the connection between diabetes and its risk factors, can lead to significant improvements in the field of diabetes research and provide better health care for this group of patients.