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  • P-ISSN 1010-0695
  • E-ISSN 2288-3339

Basic Tongue Diagnosis Indicators for Pattern Identification in Stroke Using a Decision Tree Method

Journal of Korean Medicine / Journal of Korean Medicine, (P)1010-0695; (E)2288-3339
2012, v.33 no.4, pp.1-8
Ju Ah Lee (Medical Research Division, Korea Institute of Oriental Medicine)
Jungsup Lee (Korean National Rehabilitation Center)
Mi Mi Ko (Medical Research Division, Korea Institute of Oriental Medicine)


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Abstract

Objectives: The purpose of this study was to specify major tongue diagnostic indicators and evaluate their significance in discriminating pattern identification subtypes in stroke patients. Methods: This study used a community based multi-center observational design. Participants (n=1,502) were stroke patients admitted to 11 oriental medical university hospitals between December 2006 and February 2010. To determine which tongue indicator affected each pattern identification, a decision tree analysis of the chi-square automatic interaction detector (CHAID) algorithm was performed. The chi-squared test was used as the criterion in splitting data with a p-value less than 0.05 for division, which is the main procedure for developing a decision tree. The minimum sample size for each node was specified as n =10, and branching was limited to two levels. Results: From the 9 tongue diagnostic indicators, 6 major tongue indicators (red tongue, pale tongue, yellow fur, white fur, thick fur, and teeth-marked tongue) were identified through the decision tree analysis. Furthermore, each pattern identification was composed of specific combinations of the 6 major tongue indicators. Conclusions: This study suggests that the 6 tongue indicators identified through the decision tree analysis can be used to discriminate pattern identification subtypes in stroke patients. However, it is still necessary to re-evaluate other pattern identification indicators to further the objectivity and reliability of traditional Korean medicine.

keywords
stroke, pattern identification, tongue diagnosis, tongue indicators, decision tree analysis


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