Chinese medicine pattern differentiation model for systemic lupus erythematosus based on XGBoost algorithm
Objective To construct a Chinese medicine(CM)pattern differentiation model for systemic lupus erythematosus(SLE)using the XGBoost algorithm and explore the feasibility of applying the XGBoost model for CM pattern classification.Methods Eligible cases were collected through a questionnaire survey to establish a SLE dataset.An XGBoost-based SLE CM pattern differentiation model was developed,and the random forest(RF)algorithm was used as a control for accuracy comparison.Results A total of 400 SLE patients were included in this study,including 33 males and 367 females.The top three CM patterns for SLE patients were yang deficiency of the spleen and kidney pattern,yin deficiency-induced internal heat pattern,and wind dampness and heat impediment pattern.The classification indicators and performance curve scores of the XGBoost algorithm model were overall superior to those of the RF algorithm.Conclusion XGBoost algorithm demonstrates high accuracy in CM pattern modeling and can be used for classification research in CM pattern studies.
systemic lupus erythematosusXGBoost algorithmrandom forest algorithmChinese medicine pattern
万方数据
维普
