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


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.


Data mining;Decision tree;Diabetes;Metabolic syndrome;KHANES

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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.

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Table 2. The classification results of the decision tree

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Table 3. 14 rules generated from the decision tree.

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Table 4. Comparison of the four algorithms

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Supported by : Korea University Business School


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