Abstract
Objective
To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint.
Methods
Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL).
Results
REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively.
Conclusions
The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.
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Supported by the National Natural Science Foundation of China (No. 81173199), Shanghai Sailing Program (No.15YF1412100), Young Teachers’ Training Funded Project in Shanghai University (No. ZZszy13003) and Budget for Research Shanghai Municipal Education Commission (No. 2013JW06), Chin
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Xu, J., Xu, Zx., Lu, P. et al. Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label. Chin. J. Integr. Med. 22, 867–871 (2016). https://doi.org/10.1007/s11655-016-2264-0
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DOI: https://doi.org/10.1007/s11655-016-2264-0