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

여성 비만의 유발유형 분류방법 연구

Classification Method for Four Types of Obesity in Women

대한한의학회지 / Journal of Korean Medicine, (P)1010-0695; (E)2288-3339
2003, v.24 no.1, pp.122-132
진승희 (경희대학교)
최경미 (홍익대학교)
박영배 (경희대학교)
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Abstract

Objectives : To effectively improve the treatment of obesity through Oriental medicine and to prepare basic material for proper classification of different types of obesity. Methods : After deciding on four types of obesity based on ‘DongYiBaoJian’, a questionnaire consisting of 38 items was constructed to decide which type an individual belonged to. 212 Females were requested to the complete the questionnaire. To verify that the cluster of four types of obesity was acceptable, a cluster analysis and a factor analysis were conducted as well as an evaluation on the distinction of each type. Also a canonical discriminant analysis was done to categorize the individuals into one of four types of obesity. Results : 1) Developed a reliable questionnaire consisting of 38 items for the purpose of classifying four types of obesity. 2) Obesity types were divided into four groups. Type I was designated as GanChengPi (肝乘脾類型), Type Ⅱ as PiWeiJuWang (脾胃俱旺類型), Type Ⅲ as PiWeiJuXu (脾胃俱虛類型), and Type Ⅳ as TanYin (痰飮類型). These types were verified and classified through the use of a cluster analysis as well as a factorizing analysis (p<0.05). 3) By the use of a questionnaire, four types of obesity were correctly classified with hit ratio of 87.3% and was 40.64% higher than the maximum chance criteria(Cmax) in unselected grouped. The hit ratios for Obesity Type I, Ⅱ, Ⅲ and Ⅳ were 93.3%, 93.3%, 78.6% and 50%(p<0.05). Conclusion: Further clinical research is necessary in the four types of obesity explored. By analyzing various test results, characteristics these types should be further explored.

keywords
Four types of obesity, skin elasticity, questionnaire, clustering, canonical discriminant analysis, Four types of obesity, skin elasticity, questionnaire, clustering, canonical discriminant analysis


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