Metabolic syndrome prediction model using Bayesian optimization and XGBoost based on traditional Chinese medicine features
Jianhua Zheng,
Zihao Zhang,
Jinhe Wang,
Ruolin Zhao,
Shuangyin Liu,
Gaolin Yang,
Zhengjie Liu,
Zhengyuan Deng
Affiliations
Jianhua Zheng
College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China; Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, 510630, China
Zihao Zhang
College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China
Jinhe Wang
Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, 100091, China
Ruolin Zhao
College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China
Shuangyin Liu
College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China; Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, 510630, China
Gaolin Yang
College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China
Zhengjie Liu
Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China; The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; Corresponding author. Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, 510120, China.
Zhengyuan Deng
College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China; Network and Educational Technology Center, Jinan University, Guangzhou, 510630, China
Metabolic syndrome (MetS) has a high prevalence and is prone to many complications. However, current MetS diagnostic methods require blood tests that are not conducive to self-testing, so a user-friendly and accurate method for predicting MetS is needed to facilitate early detection and treatment. In this study, a MetS prediction model based on a simple, small number of Traditional Chinese Medicine (TCM) clinical indicators and biological indicators combined with machine learning algorithms is investigated. Electronic medical record data from 2040 patients who visited outpatient clinics at Guangdong Chinese medicine hospitals from 2020 to 2021 were used to investigate the fusion of Bayesian optimization (BO) and eXtreme gradient boosting (XGBoost) in order to create a BO-XGBoost model for screening nineteen key features in three categories: individual bio-information, TCM indicators, and TCM habits that influence MetS prediction. Subsequently, the predictive diagnostic model for MetS was developed. The experimental results revealed that the model proposed in this paper achieved values of 93.35 %, 90.67 %, 80.40 %, and 0.920 for the F1, sensitivity, FRS, and AUC metrics, respectively. These values outperformed those of the seven other tested machine learning models. Finally, this study developed an intelligent prediction application for MetS based on the proposed model, which can be utilized by ordinary users to perform self-diagnosis through a web-based questionnaire, thereby accomplishing the objective of early detection and intervention for MetS.
