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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (5): 75-83    DOI: 10.11925/infotech.2096-3467.2019.0974
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Similarity Measurement of Traditional Chinese Medicine Components for Cold-hot Nature Discrimination
Wei Guohui1,2,Zhang Fengcong1,Fu Xianjun1,Wang Zhenguo1()
1Key Laboratory of Theory of TCM, Ministry of Education of China, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
2School of Science and Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China
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Abstract  

[Objective] This paper tries to measure the similarity of traditional Chinese medicine components, and then establish a discriminant method for their cold and hot natures.[Methods] Traditional Chinese medicines with similar compositions have similar medicinal properties. Therefore, we used ultraviolet spectra to characterize their components and retrieved the UV spectrum data of 61 traditional Chinese medicines. Then, we used the Mahalanobis distance to measure the similarities of these UV spectrum data. Finally, we constructed a prediction and recognition model for cold and hot natures based on the majority voting algorithm.[Results] We evaluated the proposed model with cross validation and extrapolation techniques. With the solvent of petroleum ether, areas under the ROC curve of cross validation and extrapolated prediction were 0.883 and 0.866. Predictive accuracies of cross validation and extrapolated prediction were 0.754 and 0.776. With multi-solvent comprehensive analysis, the accuracies of cross validation and extrapolation were 0.672 and 0.686.[Limitations] The data size of our study needs to be expanded.[Conclusions] The proposed model could effectively identify ultraviolet spectrum of traditional Chinese medicine components.

Key wordsNature of Chinese Traditional Medicines      Similarity      Mahalanobis Distance      Ultraviolet Spectrum      Predictive Model     
Received: 26 August 2019      Published: 15 June 2020
ZTFLH:  TP391 G354  
Corresponding Authors: Wang Zhenguo     E-mail: zhenguow@126.com

Cite this article:

Wei Guohui,Zhang Fengcong,Fu Xianjun,Wang Zhenguo. Similarity Measurement of Traditional Chinese Medicine Components for Cold-hot Nature Discrimination. Data Analysis and Knowledge Discovery, 2020, 4(5): 75-83.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0974     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I5/75

The UV Absorption Curve of Kochiae Fructus with Petroleum Ether Solvent
序号 中药 序号 中药 序号 中药
1 薄荷(寒) 21 毛知母(寒) 41 附子(热)
2 侧柏(寒) 22 墨旱莲 (寒) 42 甘松(热)
3 柴胡(寒) 23 蒲公英(寒) 43 干姜(热)
4 车前子(寒) 24 秦皮(寒) 44 高良姜(热)
5 川贝母(寒) 25 瞿麦(寒) 45 藁本(热)
6 大黄(寒) 26 生地黄(寒) 46 红花(热)
7 大青叶(寒) 27 天冬(寒) 47 厚朴(热)
8 淡竹叶(寒) 28 豨茜草(寒) 48 麻黄(热)
9 地肤子(寒) 29 栀子(寒) 49 木瓜(热)
10 防己(寒) 30 紫草(寒) 50 木香(热)
11 甘遂(寒) 31 白胡椒(热) 51 羌活(热)
12 葛根(寒) 32 白芥子(热) 52 肉桂(热)
13 瓜蒌(寒) 33 半夏(热) 53 檀香(热)
14 海藻(寒) 34 荜茇(热) 54 天南星(热)
15 黄柏(寒) 35 补骨脂(热) 55 威灵仙(热)
16 黄连(寒) 36 苍术(热) 56 吴茱萸(热)
17 金银花(寒) 37 草豆蔻(热) 57 细辛(热)
18 龙胆(寒) 38 陈皮(热) 58 仙茅(热)
19 芦荟(寒) 39 川芎(热) 59 延胡索(热)
20 络石藤(寒) 40 杜仲(热) 60 淫羊藿(热)
61 炙延胡索(热)
61 Traditional Chinese Medicines
λ and K
">
ACC Values with Different λ and K
The ROC Values of UV Nature Identification with Four Solvents
溶剂 AUC ACC
无水乙醇 0.673 0.510
氯仿 0.759 0.602
蒸馏水 0.752 0.675
石油醚 0.883 0.754
AUC and ACC Values of Chinese Medicine Nature Identification with Four Solvents
分类算法 AUC ACC
人工神经网络 0.663 0.590
支持向量机 0.795 0.738
极限学习机 0.587 0.525
本文模型 0.883 0.754
Stability Comparison of Single Solvent Models
分类算法 ACC
人工神经网络 0.560
支持向量机 0.623
极限学习机 0.506
本文模型 0.672
Stability Comparison of Multiple Solvent Models
分类算法 AUC ACC
人工神经网络 0.650±0.09 0.620±0.01
支持向量机 0.790±0.09 0.695±0.09
极限学习机 0.676±0.09 0.605±0.06
本文模型 0.866±0.03 0.776±0.08
Comparison of Single Solvent Models
分类算法 ACC
人工神经网络 0.590±0.04
支持向量机 0.673±0.09
极限学习机 0.578±0.06
本文模型 0.686±0.06
Comparison of Multiple Solvent Models
真实值

预测值
寒性药 热性药
寒性药 22 8
热性药 7 24
Confusion Matrix of Nature Prediction of 61 Chinese Medicines
指标 寒性药 热性药
召回率 0.733 0.774
精确率 0.758 0.750
F值 0.745 0.762
The Performance of Nature Prediction of 61 Chinese Medicines
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