Application research on quantitative prediction of TCM syndrome differentiation based on ensemble learning Online publication date: Mon, 09-Nov-2020
by Huaixin Liang; Xin Yang; Shaoxiong Li; Siheng Chen; Xiaoqing Zhang
International Journal of Computer Applications in Technology (IJCAT), Vol. 64, No. 1, 2020
Abstract: A quantitative prediction method for TCM syndrome element identification based on ensemble learning is proposed. Four comparative experiments were designed. Firstly, eight mainstream learners were used to perform the regression prediction based on the symptoms and syndrome values using the quantitative data of clinical TCM syndrome differentiation. Secondly, five learners with excellent prediction performance were selected to design three integrated learners including homogeneous static integrated learner, heterogeneous static integrated learner and dynamic one, where the heterogeneous integrator used as the learner weight coefficient to weigh up its significance. By comparing the MAE, MSE and R² of the three ensemble learning methods in the four syndrome differentiation groups, it is found that the regression effect based on heterogeneous ensemble learning is the best (MAE: 0.012, MSE: 4.55E-04, R²: 0.733), and the principal sequential evaluation of syndrome elements gained relatively matching degree, which had proved the feasibility of application on the method proposed in the quantitative prediction of clinical TCM syndromes.
Online publication date: Mon, 09-Nov-2020
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org