Application of machine learning in Chinese medicine differentiation of dampness-heat pattern in patients with type 2 diabetes mellitus
Xinyu Liu,
Xiaoqiang Huang,
Jindong Zhao,
Yanjin Su,
Lu Shen,
Yuhong Duan,
Jing Gong,
Zhihai Zhang,
Shenghua Piao,
Qing Zhu,
Xianglu Rong,
Jiao Guo
Affiliations
Xinyu Liu
Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
Xiaoqiang Huang
Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, 510006, China
Jindong Zhao
The First Affiliated Hospital of Anhui University of Chinese, Hefei, 230031, China
Yanjin Su
Shaanxi University of Chinese Medicine, Xi'an, 712046, China
Lu Shen
Shaanxi Provincial Hospital of Traditional Chinese Medicine, Xi'an, 710003, China
Yuhong Duan
Affiliated Hospital of Shannxi University of Chinese Medicine, Xi'an, 712000, China
Jing Gong
Department of Integrated Traditional Chinese and Western Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
Zhihai Zhang
The First Affiliated Hospital of Xiamen University, Xiamen, 361003, China
Shenghua Piao
Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
Qing Zhu
Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
Xianglu Rong
Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China
Jiao Guo
Guangdong Metabolic Diseases Research Center of Integrated Chinese and Western Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Key Laboratory of Glucolipid Metabolic Disorder, Ministry of Education of China, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Institute of Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, 510006, China; Corresponding author. 280 Wai Huan Dong Road, Guangdong Pharmaceutical University, Guangzhou Higher Education Mega Center, Guangzhou, 510006, China.
Background: China has become the country with the largest number of people with type 2 diabetes mellitus (T2DM), and Chinese medicine (CM) has unique advantages in preventing and treating T2DM, while accurate pattern differentiation is the guarantee for proper treatment. Objective: The establishment of the CM pattern differentiation model of T2DM is helpful to the pattern diagnosis of the disease. At present, there are few studies on dampness-heat pattern differentiation models of T2DM. Therefore, we establish a machine learning model, hoping to provide an efficient tool for the pattern diagnosis of CM for T2DM in the future. Methods: A total of 1021 effective samples of T2DM patients from ten CM hospitals or clinics were collected by a questionnaire including patients' demographic and dampness-heat-related symptoms and signs. All information and the diagnosis of the dampness-heat pattern of patients were completed by experienced CM physicians at each visit. We applied six machine learning algorithms (Artificial Neural Network [ANN], K-Nearest Neighbor [KNN], Naïve Bayes [NB], Support Vector Machine [SVM], Extreme Gradient Boosting [XGBoost] and Random Forest [RF]) and compared their performance. And then we also utilized Shapley additive explanation (SHAP) method to explain the best performance model. Results: The XGBoost model had the highest AUC (0.951, 95% CI 0.925–0.978) among the six models, with the best sensitivity, accuracy, F1 score, negative predictive value, and excellent specificity, precision, and positive predictive value. The SHAP method based on XGBoost showed that slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis. The slippery pulse or rapid-slippery pulse, sticky stool with ungratifying defecation also performed an important role in this diagnostic model. Furthermore, the red tongue acted as an important tongue sign for the dampness-heat pattern. Conclusion: This study constructed a dampness-heat pattern differentiation model of T2DM based on machine learning. The XGBoost model is a tool with the potential to help CM practitioners make quick diagnosis decisions and contribute to the standardization and international application of CM patterns.
