NDLI: A novel method for quantitative diagnosis based on decision tree in Traditional Chinese Medicine

Content Provider	IEEE Xplore Digital Library
Author	Hui-Yan Wang
Copyright Year	2009
Description	Author affiliation: College of Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou, 310018, China (Hui-Yan Wang)
Abstract	Traditional Chinese Medicine (TCM) is one of the most important complementary and alternative medicines. In this paper, a novel computerized diagnostic method based on decision tree (DT) is proposed for promoting standardization and popularization of TCM diagnosis. In TCM, the symptoms are often high dimensional. Although DT induction algorithm has a feature selection scheme included in its learning performance, but this scheme is not optimal. The redundant and irrelevant symptoms may degrade the performance of the induced classifier. In this work, we utilize feature selection algorithm prior to the learning phase. The experiments show that the proposed method constructs much simpler tree and obtains relative reliable predictions. The rate of predictive accuracy in diagnosing apoplexy reaches 94.15%. The results suggest that the method proposed is feasible and effective and can be expected to be useful in the modernization of TCM.
Starting Page	344
Ending Page	349
File Size	195402
Page Count	6
File Format	PDF
ISBN	9781424437023
DOI	10.1109/ICMLC.2009.5212538
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Medical diagnostic imaging Diseases Machine learning Predictive models Bayesian methods Cybernetics Degradation Accuracy Niobium compounds Computerized diagnosis Traditional Chinese Medicine Decision tree Symptom selection
Content Type	Text
Resource Type	Article

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