사상체질 진단검사를 위한 데이터마이닝 알고리즘 연구

• Published in: 동의생리병리학회지 Vol. 23; no. 6; pp. 1234 - 1240
• Main Authors: 홍진우(Jin Woo Hong), 김영인(Young In Kim), 박소정(So Jung Park), 김병철(Byoung Chul Kim), 엄일규(Il Kyu Eom), 황민우(Min Woo Hwang), 신상우(Sang Woo Shin), 김병주(Byung Joo Kim), 권영규(Young Kyu Kwon), 채한(Han Chae)
• Format: Journal Article
• Language: Korean
• Published: 한의병리학회 2009
• Subjects: 한의학; clustering algorithm; wikato environment knowledge analysis; pattern identification; sasang type diagnosis; data field selection; sensitivity and specificity
• ISSN: 1738-7698; 2288-2529; 2283-2529

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.