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대사증후군 환자 가운데 당뇨환자를 찾기 위한 규칙 도출

Deriving rules for identifying diabetic among individuals with metabolic syndrome

  • 투고 : 2018.08.16
  • 심사 : 2018.11.20
  • 발행 : 2018.11.28

초록

본 연구의 목적은 대사증후군이 당뇨병으로 확대되는 것을 방지하는데 이용할 수 있는 구체적인 분류 규칙을 도출하는 것이다. 좀 더 구체적으로 말하면, 대사증후군을 앓고 있는 사람들을 당뇨병이 없는 사람 (class 0)과 당뇨병이 있는 사람(class 1)으로 구별해 내는 분류하는 규칙을 찾는 것이다. 본 연구는 국민건강영양조사 데이터를 수집하여 데이터 전처리 과정들을 거친 후 의사결정나무를 구축하였다. 생성된 의사결정나무로부터 유용한 5개의 분류 규칙을 도출하였는데, 이들의 평균 분류 정확도는 75.8%이었다. 또한, 생성된 의사결정나무로부터 고혈압 여부와 허리둘레가 class 0 그룹과 class 1 그룹으로 분류하는데 있어서 중요한 요인임을 알 수 있었다. 이번 연구 결과는 의사들이 향후 대사증후군 환자가 당뇨환자가 되지 않도록 치료하는데 좋은 지침이 될 것으로 기대된다.

키워드

데이터 마이닝;의사결정나무;당뇨병;대사증후군;국민건강영양조사

DJTJBT_2018_v16n11_363_f0001.png 이미지

Fig. 1. The decision tree built from the training dataset. The bar at the bottom of the figure indicates the proportion of each class in the leaf node.

Table 1. Details of the features remained after pre-processing.

DJTJBT_2018_v16n11_363_t0001.png 이미지

Table 2. The classification results of the decision tree

DJTJBT_2018_v16n11_363_t0002.png 이미지

Table 3. 14 rules generated from the decision tree.

DJTJBT_2018_v16n11_363_t0003.png 이미지

Table 4. Comparison of the four algorithms

DJTJBT_2018_v16n11_363_t0004.png 이미지

과제정보

연구 과제 주관 기관 : Korea University Business School

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