Title: Application research on quantitative prediction of TCM syndrome differentiation based on ensemble learning
Authors: Huaixin Liang; Xin Yang; Shaoxiong Li; Siheng Chen; Xiaoqing Zhang
Addresses: Qinhuangdao Centre for Disease Control and Prevention, Qinhuangdao City, Hebei Province, China; Tianjin Research Institute for Advanced Equipment, Tsinghua University, Dongli District, Tianjin, China ' Beijing Time CIIC Technology Co., Ltd., Haidian District, Beijing, China ' Shanghai University of Traditional Chinese Medicine, Pudong New District, Shanghai, China ' Guangdong Province Hospital of Traditional Chinese Medicine Zhuhai Branch, Zhuhai City, Guangdong Province, China ' Beijing University of Chinese Medicine, Chaoyang District, Beijing, China
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.
Keywords: ensemble learning; syndrome differentiation; regression prediction; multi-objective learning; traditional Chinese medicine; syndrome element model; quantitative prediction; artificial intelligence; clinical auxiliary diagnosis.
International Journal of Computer Applications in Technology, 2020 Vol.64 No.1, pp.46 - 56
Received: 31 Dec 2019
Accepted: 25 May 2020
Published online: 21 Oct 2020 *