TANG Cheng;ZHAO Xudong;CHEN Yu;SU Guanxun;ZHI Yushang;LIU Xiaoyu;WANG Shidong;Dongzhimen Hospital, Beijing University of Chinese Medicine;Objective: Using machine learning(ML) methods and incorporating symptoms of Yin deficiency syndrome in type 2 diabetes mellitus(T2DM) as variables, this study aims to construct a prediction model for glycosylated hemoglobin(HbA1c) control levels in T2DM with Yin deficiency syndrome, providing reference to enrich the connotation of Yin deficiency syndrome in T2DM and optimize HbA1c management.Methods: Data of T2DM patients with Yin deficiency syndrome treated from January 1,2020,to December 1,2022,at the Nephrology and Endocrinology Department II of Dongcheng Campus of Dongzhimen Hospital and the Nephrology and Endocrinology Department III of Tongzhou Campus of Dongzhimen Hospital affiliated to Beijing University of Chinese Medicine, Hunan University of Chinese Medicine, and the First Affiliated Hospital to Xiamen University were collected, including basic information and symptoms of Yin deficiency syndrome.LASSO regression and stepwise regression were used jointly to screen features related to Yin deficiency syndrome.Based on the selected features, various machine learning models were constructed for training, validation, and testing.Shapley additive explanations(SHAP) were used to interpret the optimal model.Results: A total of 1,163 T2DM patients with Yin deficiency syndrome were included, of whom 747 had HbA1c ≥7% and 416 had HbA1c <7%.Through LASSO and stepwise regression, 13 characteristic variables of yin deficiency syndrome in T2DM were screened, ultimately including 7 symptom variables: craving for cold drinks, dry mouth and throat, night sweats, aversion to heat, irritability, dry stools, and dry eyes.Internal and external validations, comparing multiple models across various metrics(accuracy, precision, specificity, recall, F1 score, and AUC),showed that the Random Forest(RF) model performed the best, with an accuracy of 0.814,recall of 0.721,precision of 0.754,specificity of 0.868,F1 score of 0.738,and an AUC of 0.865.SHAP analysis indicated that dry mouth and throat, craving for cold drinks, night sweats, dry stools, irritability, aversion to heat, and dry eyes are significant features influencing HbA1c control, with dry mouth and throat, craving for cold drinks, and night sweats being the top three most important variables for HbA1c management.Conclusion: This study constructed a predictive model for HbA1c control in T2DM patients based on the characteristic symptoms of yin deficiency syndrome, demonstrating good predictive performance and clinical applicability, helping to enrich the modern understanding of yin deficiency in T2DM and providing references for personalized glycemic management and optimization of TCM treatment plans.
2026 03 v.41;No.334 [Abstract][OnlineView][Download 520K]