Classification Algorithm Analysis of TCM Syndrome of Menopausal Syndrome Based on Data Mining
10.3969/j.issn.1005-5304.2016.01.009
- VernacularTitle:基于数据挖掘的围绝经期综合征中医证候分类算法分析
- Author:
Hongjin WU
;
Jiatuo XU
;
Zhifeng ZHANG
;
Liping TU
;
Tingting ZHANG
;
Lianwei XU
;
Qiaolian LIU
- Publication Type:Journal Article
- Keywords:
menopausal syndrome;
TCM syndrome;
data mining;
classification algorithm;
training samples;
margin curve
- From:
Chinese Journal of Information on Traditional Chinese Medicine
2016;(1):39-42
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish the optimum syndrome classification method by using the technology of modern TCM diagnosis and artificial intelligence analysis method for menopausal syndrome differentiation of TCM. Methods Diagnostic information of menopausal syndrome patients was collected and syndromes were classified according to TCM syndrome differentiation standard. Three kinds of common data mining classification algorithm, Bayesian network, K-nearest neighbors and support vector machine, were used for analysis on information data of the four methods of diagnosis of menopausal syndrome.Results The time, classification accuracy, coverage rate and margin curve of establishing TCM syndrome model by the three kinds of algorithm methods under the circumstances of same training and data. The influence of the number of training samples of 3 kinds of algorithm methods was analyzed, and the model established by the three kinds of algorithms was evaluated.Conclusion Bayesian network algorithm is better than the other two methods in the menopausal syndrome classification effect.
