Korean J Intern Med.  2012 Jun;27(2):197-202. 10.3904/kjim.2012.27.2.197.

Comorbidity Study on Type 2 Diabetes Mellitus Using Data Mining

• Kim HS
• Shin AM
• Kim MK
• Kim YN

• KMID: 1776547
• DOI: http://doi.org/10.3904/kjim.2012.27.2.197

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, > or = 3%; confidence, > or = 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.

Keyword

Diabetes mellitus, type 2; Comorbidity; Data mining

MeSH Terms

Academic Medical Centers/statistics & numerical data
Algorithms
Case-Control Studies
Chi-Square Distribution
Comorbidity
Data Mining/*statistics & numerical data
Databases, Factual/statistics & numerical data
Diabetes Mellitus, Type 2/*epidemiology
Dyslipidemias/epidemiology
Humans
Hypertension/epidemiology
Republic of Korea/epidemiology
Stroke/epidemiology