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Data mining Algorithms for the Development of Sasang Type Diagnosis  

Hong, Jin-Woo (Division of Clinical Medicine, School of Korean Medicine, Pusan National University)
Kim, Young-In (Department of Biomedical Engineering, College of Natural Resource and Life Science, Pusan National University)
Park, So-Jung (Division of Longevity and Biofunctional Medicine, School of Korean Medicine, Pusan National University)
Kim, Byoung-Chul (Department of Biomedical Engineering, College of Natural Resource and Life Science, Pusan National University)
Eom, Il-Kyu (School of Electrical Engineering, Pusan National University)
Hwang, Min-Woo (Division of Clinical Medicine, School of Korean Medicine, Pusan National University)
Shin, Sang-Woo (Division of Applied Medicine, School of Korean Medicine, Pusan National University)
Kim, Byung-Joo (Division of Longevity and Biofunctional Medicine, School of Korean Medicine, Pusan National University)
Kwon, Young-Kyu (Division of Longevity and Biofunctional Medicine, School of Korean Medicine, Pusan National University)
Chae, Han (Division of Longevity and Biofunctional Medicine, School of Korean Medicine, Pusan National University)
Publication Information
Journal of Physiology & Pathology in Korean Medicine / v.23, no.6, 2009 , pp. 1234-1240 More about this Journal
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.
Keywords
wikato environment knowledge analysis; sasang type diagnosis; pattern identification; sensitivity and specificity; clustering algorithm; data field selection;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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1 허 준, 최병주. (클레멘타인을 이용한)데이터마이닝 : 입문편. SPSS 아카데미, 서울, 2001
2 이창희. Feature selection을 이용한 중요변수 선택방법에 관한 연구. 석사학위논문, 중앙대학교, 2007
3 Frank, E., Hall, M., Trigg, L., Holmes, G., Witten, I.H. Data mining in bioinformatics using Weka. Bioinformatics. 20(15):2479-2481, 2004   DOI   ScienceOn
4 박성식, 최재영. 의사결정나무법을 이용한 설문지의 응답특성에 대한 임상적 검토. 사상체질의학회지 15(3):177-186, 2003   과학기술학회마을
5 Zhang, Q., Lee, K.J., Whangbo, T.K. K-mean and double cross-validation algorithm for LS-SVM in Sasang typology classification. Proceedings of the IEEE International Conference on Automation and Logistics. August pp 18-21, 2007, Jinan, China, pp 426-430, 2007   DOI
6 김선호, 고병희, 송일병. 사상체질분류검사지(QSCCⅡ)의 표준화 연구. 사상의학회지 7(1):187-246, 1995
7 Savova, G.K., Ogren, P.V., Duffy, P.H., Buntrock, D.J., Chute, C.G. Mayo clinic NLP system for patient smoking status identification. Journal of the American Medical Informatics Association. 15(1):25-28, 2008   DOI
8 Chae, H., Park, S.H., Lee, S.J., Kim, M.g., Wedding, D., Kwon, Y.K. Psychological profile of Sasang typology: A systematic Review. eCAM, 6: 21-29, 2009   DOI
9 박은경, 이영섭, 박성식. 의사결정나무법을 이용한 체질진단에 관한 연구. 사상체질의학회지 13(2):144-155, 2001   과학기술학회마을
10 Park, S.H., Kim, M.G., Lee, S.J., Kim, J.Y., Chae, H. Temperament and Character Profiles of Sasang Typology in an Adult Clinical Sample. Evid Based Complement Alternat Med. doi:10.1093/ecam/nep034, Advance Access published April 20, 2009   DOI   ScienceOn
11 Chae, H., Lyoo, I.K., Lee, S.J., Cho, S., Bae, H., Hong, M., Shin, M. An alternative way to individualized medicine: psychological and physical traits of Sasang typology. Journal of Alternative and Complementary Medicine. 9(4):519-528, 2003   DOI   ScienceOn
12 이수진, 박수현, 고유선, 박수진, 엄일규, 김병철, 김영인, 백진웅, 김명근, 권영규, 채한. 임피던스 분석을 활용한 사상인의 신체계측 연구. 동의생리병리학회지 23(2):433-437, 2009   과학기술학회마을
13 Michael, J.A. Berry and Gordon Linoff, Data Mining Techniques: For Marketing, Sales, and Customer Support, John Wiley & Sons, Inc., 1997
14 Witten, I.H., Frank, E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, C.A, 2000
15 이덕기. 데이터마이닝 소프트웨어의 효율성에 관한 비교 연구. 석사학위논문. 호서대학교, 2001
16 황상문, 박소정, 강기림, 권영규, 채 한. 사상체질 진단검사 타당성 분석지표의 일반화 연구. 동의생리병리학회지 23(5):950-957, 2009   과학기술학회마을
17 채 한, 황상문, 엄일규, 김병철, 김영인, 김병주, 권영규. 신경망을 사용한 사상체질 진단검사 개발 연구. 동의생리병리학회지 23(4):765-771, 2009   과학기술학회마을
18 Lukman, S., He, Y., Hui, S.C. Computational medthos for Traditional Chinese Medicine: A survey. Computer methods and programs in biomedicine. 88: 283-294, 2007   DOI   ScienceOn
19 이수진, 김명근, 채 한. 사상체질 진단검사 타당성분석에 대한 연구. 대한한의학회지 29(1):7-14, 2008   과학기술학회마을
20 진희정, 문진석, 고성호, 구임회, 이시우, 이도현, 송미영, 김종열. 사상체질 의사결정시스템 구축을 위한 체질 진단 자료를 이용한 예비연구. 한국한의학연구원논문집, 13(2):75-81, 2007
21 채 한, 박수잔, 이수진, 고광찬. 사상 유형학의 성격심리학적 고찰. 대한한의학회지 25(2):151-164, 2004   과학기술학회마을