Jin Xu1, Zhao-Xia Xu1, Ping Lu2, Rui Guo1, Hai-Xia Yan1, Wen-Jie Xu1, Yi-Qin Wang3, Chun-Ming Xia2. 1. School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China. 2. Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai, 200237, China. 3. School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China. wangyiqin2380@sina.com.
Abstract
OBJECTIVE: To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. METHODS: Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL). RESULTS: REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. CONCLUSIONS: The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.
OBJECTIVE: To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. METHODS: Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm (REAL). RESULTS: REAL was employed to establish a Xin (Heart) qi defificiency, Xinyang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. CONCLUSIONS: The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.
Entities:
Keywords:
Chinese medicine; multi-label learning algorithm; syndrome differentiation
Authors: Jasjit S Suri; Mrinalini Bhagawati; Sudip Paul; Athanasios D Protogerou; Petros P Sfikakis; George D Kitas; Narendra N Khanna; Zoltan Ruzsa; Aditya M Sharma; Sanjay Saxena; Gavino Faa; John R Laird; Amer M Johri; Manudeep K Kalra; Kosmas I Paraskevas; Luca Saba Journal: Diagnostics (Basel) Date: 2022-03-16