Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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Deriving rules for identifying diabetic among individuals with metabolic syndrome

대사증후군 환자 가운데 당뇨환자를 찾기 위한 규칙 도출

  • Received : 2018.08.16
  • Accepted : 2018.11.20
  • Published : 2018.11.28
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Abstract

The objective of this study is to derive specific classification rules that could be used to prevent individuals with Metabolic Syndrome (MS) from developing diabetes. Specifically, we aim to identify rules which classify individuals with MS into those without diabetes (class 0) and those with diabetes (class 1). In this study we collected data from Korean National Health and Nutrition Examination Survey and built a decision tree after data pre-processing. The decision tree brings about five useful rules and their average classification accuracy is quite high (75.8%). In addition, the decision tree showed that high blood pressure and waist circumference are the most influential factors on the classification of the two groups. Our research results will serve as good guidelines for clinicians to provide better treatment for patients with MS, such that they do not develop diabetes.

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.

Keywords

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

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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

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