Analysis of traditional Chinese medicine prescriptions based on support vector machine and analytic hierarchy process.
10.19540/j.cnki.cjcmm.20180419.007
- Author:
Zi-Li JIN
1
;
Jian-Xing HU
1
;
Hong-Wei JIN
1
;
Liang-Ren ZHANG
1
;
Zhen-Ming LIU
1
Author Information
1. State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China.
- Publication Type:Journal Article
- Keywords:
analytic hierarchy process;
machine learning;
support vector machine;
traditional Chinese medicine;
traditional Chinese medicine inheritance support system(TCMISS)
- From:
China Journal of Chinese Materia Medica
2018;43(13):2817-2823
- CountryChina
- Language:Chinese
-
Abstract:
Combined use of drugs is a hot spot in the research of new drugs nowadays, and traditional Chinese medicine (TCM) is a classic practice in the combined use of drugs. In this paper, the compatibility of TCM prescriptions and the related properties of composed herbs were calculated and studied to verify and discuss the feasibility of the results in guiding compatibility. Research Group on New Drug Design, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences had established a structured database of TCM prescriptions by using traditional Chinese medicine inheritance support system (TCMISS V2.0), including 4 012 prescription compatibilities, 2 072 drug components, 381 kinds of TCM diseases, 316 kinds of TCM syndromes and 26 kinds of drug properties. On the basis of the created database above, Support Vector Machine (SVM) was used to analyze the prescription compatibility data and establish a model for predicting feasibility of drug compatibilities. Analytic Hierarchy Process (AHP) and cluster analysis were used to study the influence of drug properties in the rationality of prescription compatibility. The computational results showed that the accuracy in efficacy prediction of two data sets, i.e. prescription-disease and prescription-syndrome, was up to 90% in the linear SVM model. The macro₋averaging and micro₋averaging of the two models were around 0.92, 0.46, respectively. After AHP mapping, most of the incompatible combinations showed significant difference with other drug combinations during the clustering process in the vertical icicle, indicating that the proper machine learning algorithm can be used to lay the foundation for further exploring the combination rules in TCM and establishing more detailed drug-disease and syndrome predicting models, and provide theoretical guidance for the study of the combined use of drugs to a certain degree.
