Novel statistical classification model of type 2 diabetes mellitus patients for tailor-made prevention using data mining algorithm

Koichi Miyaki, Izumi Takei, Kenji Watanabe, Hiroshi Nakashima, Kiyoaki Watanabe, Kazuyuki Omae

Research output: Contribution to journalArticle

27 Citations (Scopus)


To estimate the usefulness of data mining algorithms for extracting risk predictors of diabetic vascular complications in proper order in the future, we tried applying the Classification and Regression Trees (CART) method to the prevalence data of 165 type 2 diabetic outpatients and already known risk factors. Among the 6 categorical and 15 continuous risk factors, age (cutoff: 65.4) was the best predictor for classifying patients into groups with and without macroangiopathy (p=0.000). Body weight (cutoff: 53.9) was the best predictor (p=0.006) in the older group (age >65.4), whereas systolic blood pressure (cutoff: 144.5) was the best predictor in the remaining group (p=0.002). Age (cutoff: 64.8) was also the best predictor for categorizing them into groups with and without microangiopathy (p=0.000). In the older group (age >64.8), BMI (cutoff: 21.5) was the best predictor (p=0.001), whereas morbidity term (cutoff: 15.5) was the best predictor in the other group (p=0.010). Because the orders and values of all risk factors and cutoff points mined were reasonable clinically, this method may have the potential to highlight predictors in order of importance to apply tailor-made prevention of diabetic vascular complications.

Original languageEnglish
Pages (from-to)243-248
Number of pages6
JournalJournal of epidemiology
Issue number3
Publication statusPublished - 2002 May



  • Classification and regression trees
  • Complications
  • Data mining
  • Tailor-made prevention
  • Type 2 diabetes

ASJC Scopus subject areas

  • Epidemiology

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