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J Diabetes Sci Technol. 2011 Nov 1;5(6):1549-56.

Data-mining technologies for diabetes: a systematic review.

Author information

1
Informatics Institute, University of Missouri, Columbia, Missouri, USA.

Abstract

BACKGROUND:

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

METHOD:

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.

PMID:
22226277
PMCID:
PMC3262726
DOI:
10.1177/193229681100500631
[Indexed for MEDLINE]
Free PMC Article

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