• 동의생리병리학회지
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• 제23권6호
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• pp.1234-1240
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• 2009

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.