Data-mining technologies for diabetes: a systematic review.
BACKGROUND
The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research.


METHODS
We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes.


RESULTS
The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients.


CONCLUSIONS
Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients.
Informatics Institute, University of Missouri, Columbia, Missouri, USA.
Miroslav
M
Marinov
Abu Saleh Mohammad
AS
Mosa
Illhoi
I
Yoo
Suzanne Austin
SA
Boren
D057225:Data Mining / Q000379:methods*; D003920:Diabetes Mellitus; D006801:Humans; D012107:Research Design