사상체질 진단검사를 위한 데이터마이닝 알고리즘 연구
Data mining Algorithms for the Development of Sasang Type Diagnosis
1
조회
0
다운로드
6
인용
0
참고문헌
키워드
초록
This study was to compare the effectiveness and validity of various data-mining algorithm for Sasang type diagnostic test. We compared the sensitivity and specificity index of nine attribute selection and eleven class classification algorithms with 31 data-set characterizing Sasang typology and 10-fold validation methods installed in Waikato Environment Knowledge Analysis (WEKA). The highest classification validity score can be acquired as follows; 69.9 as Percentage Correctly Predicted index with Naive Bayes Classifier, 80 as sensitivity index with LWL/Tae-Eum type, 93.5 as specificity index with Naive Bayes Classifier/So-Eum type. The classification algorithm with highest PCP index of 69.62 after attribute selection was Naive Bayes Classifier. In this study we can find that the best-fit algorithm for traditional medicine is case sensitive and that characteristics of clinical circumstances, and data-mining algorithms and study purpose should be considered to get the highest validity even with the well defined data sets. It is also confirmed that we can't find one-fits-all algorithm and there should be many studies with trials and errors. This study will serve as a pivotal foundation for the development of medical instruments for Pattern Identification and Sasang type diagnosis on the basis of traditional Korean Medicine.
Abstract
This study was to compare the effectiveness and validity of various data-mining algorithm for Sasang type diagnostic test. We compared the sensitivity and specificity index of nine attribute selection and eleven class classification algorithms with 31 data-set characterizing Sasang typology and 10-fold validation methods installed in Waikato Environment Knowledge Analysis (WEKA). The highest classification validity score can be acquired as follows; 69.9 as Percentage Correctly Predicted index with Naive Bayes Classifier, 80 as sensitivity index with LWL/Tae-Eum type, 93.5 as specificity index with Naive Bayes Classifier/So-Eum type. The classification algorithm with highest PCP index of 69.62 after attribute selection was Naive Bayes Classifier. In this study we can find that the best-fit algorithm for traditional medicine is case sensitive and that characteristics of clinical circumstances, and data-mining algorithms and study purpose should be considered to get the highest validity even with the well defined data sets. It is also confirmed that we can't find one-fits-all algorithm and there should be many studies with trials and errors. This study will serve as a pivotal foundation for the development of medical instruments for Pattern Identification and Sasang type diagnosis on the basis of traditional Korean Medicine.
참고문헌 (21)
- 허준, "(클레멘타인을 이용한)데이터마이닝 : 입문편" (2001), SPSS 아카데미
- 이창희, "Feature selection을 이용한 중요변수 선택방법에 관한 연구", 석사, pp.2007
- Michael, J.A., "Berry and Gordon Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support" (1997), John Wiley & Sons, Inc.
- Frank, E., "Data mining in bioinformatics using Weka", Bioinformatics, 20(15), pp.2479~2481 (2004)
- 이덕기, "데이터마이닝 소프트웨어의 효율성에 관한 비교 연구", 석사, pp.2001
- 이수진, "사상체질 진단검사 타당성 분석에 대한 연구", 대한한의학회지, 29(1), pp.7~14 (2008), 대한한의학회
- 황상문, "사상체질 진단검사 타당성 분석지표의 일반화 연구", 동의생리병리학회지, 23(5), pp.950~957 (2009), 대한동의병리학회
- Chae, H., "An alternative way to individualized medicine: psychological and physical traits of Sasang typology", Journal of Alternative and Complementary Medicine, 9(4), pp.519~528 (2003)
- 채한, "신경망을 사용한 사상체질 진단검사 개발 연구", 동의생리병리학회지, 23(4), pp.765~771 (2009), 대한동의병리학회
- 박은경, "의사결정나무법을 이용한 체질진단에 관한 연구", 사상체질의학회지, 13(2), pp.144~155 (2001)
- 진희정, "사상체질 의사결정시스템 구축을 위한 체질 진단 자료를 이용한 예비연구", 한국한의학연구원논문집, 13(2), pp.75~81 (2007)
- 박성식, "의사결정나무법을 이용한 설문지의 응답특성에 대한 임상적 검토", 사상체질의학회지, 15(3), pp.177~186 (2003)
- Zhang, Q., "K-mean and double cross-validation algorithm for LS-SVM in Sasang typology classification", Proceedings of the IEEE International Conference on Automation and Logistics, pp.18~21 (2007)
- Witten, I.H., "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations" (2000), Morgan Kaufmann
- 김선호, "사상체질분류검사지(QSCCⅡ)의 표준화 연구", 7사상의학회지, 7(1), pp.187~246 (1995)
- 이수진, "임피던스 분석을 활용한 사상인의 신체계측 연구", 동의생리병리학회지, 23(2), pp.433~437 (2009), 대한동의병리학회
- Savova, G.K., "Mayo clinic NLP system for patient smoking status identification", Journal of the American Medical Informatics Association, 15(1), pp.25~28 (2008)
- Park, S.H., "Temperament and Character Profiles of Sasang Typology in an Adult Clinical Sample", Evid Based Complement Alternat Med (2009)
- Chae, H., "Psychological profile of Sasang typology: A systematic Review", eCAM, 6, pp.21~29 (2009)
- 채한, "사상 유형학의 성격심리학적 고찰", 대한한의학회지, 25(2), pp.151~164 (2004), 대한한의학회
- Lukman, S., "Computational medthos for Traditional Chinese Medicine: A survey", Computer methods and programs in biomedicine, 88, pp.283~294 (2007)