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. 2012 Jun;27(2):197-202.
doi: 10.3904/kjim.2012.27.2.197. Epub 2012 May 31.

Comorbidity study on type 2 diabetes mellitus using data mining

Hye Soon Kim  1 , A Mi Shin, Mi Kyung Kim, Yoon Nyun Kim
Affiliations

Comorbidity study on type 2 diabetes mellitus using data mining

Hye Soon Kim et al. Korean J Intern Med. 2012 Jun.

Abstract

Background/aims: The aim of this study was to analyze comorbidity in patients with type 2 diabetes mellitus (T2DM) by using association rule mining (ARM).

Methods: We used data from patients who visited Keimyung University Dongsan Medical Center from 1996 to 2007. Of 411,414 total patients, T2DM was present in 20,314. The Dx Analyze Tool was developed for data cleansing and data mart construction, and to reveal associations of comorbidity.

Results: Eighteen associations reached threshold (support, ≥ 3%; confidence, ≥ 5%). The highest association was found between T2DM and essential hypertension (support, 17.43%; confidence, 34.86%). Six association rules were found among three comorbid diseases. Among them, essential hypertension was an important node between T2DM and stroke (support, 4.06%; confidence, 8.12%) as well as between T2DM and dyslipidemia (support, 3.44%; confidence, 6.88%).

Conclusions: Essential hypertension plays an important role in the association between T2DM and its comorbid diseases. The Dx Analyze Tool is practical for comorbidity studies that have an enormous clinical database.

Keywords: Comorbidity; Data mining; Diabetes mellitus, type 2.

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Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Figures

Figure 1
Figure 1
Schematic diagram of the study workflow.
See this image and copyright information in PMC

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