ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

Objective To explore the predictive value of triglyceride-glucose index(TyG),C-reactive pro-tein to albumin ratio(CAR),and microRNA-21(miR-21)for the prognosis of ischemic heart failure(IHF)patients with type 2 diabetes(T2DM).Methods The medical records of totally 400 IHF patients with T2DM who were admitted to the Sixth People's Hospital Affiliated to Shanghai Jiao Tong University from January 2022 to December 2023 were selected and divided into a favorable prognosis group(n=318)and a poor prog-nosis group(n=82)based on whether major adverse cardiovascular events(MACE)occurred within 6 months after diagnosis.General information,along with TyG,CAR,and miR-21 levels,was gathered for both groups.Univariate analysis and multivariate Logistic regression were applied to identify the factors influencing poor prognosis in IHF patients with T2DM.Receiver operating characteristic(ROC)curves were plotted to assess the predictive capacity of TyG,CAR,and miR-21 for poor prognosis in these patients.Results Univari-ate analysis showed that there were statistically significant differences in the history of hypertension,history of coronary heart disease,advanced age,TyG,CAR,and miR-21 between the two groups(P＜0.05).Multiva-riate Logistic regression indicated that advanced age,coronary artery disease,TyG,CAR,and miR-21 were in-dependent risk factors for poor prognosis in IHF patients with T2DM(P＜0.05).The area under the curve of the combined detection of TyG,CAR,and miR-21 for poor prognosis in IHF patients with T2DM was 0.841,with sensitivity of 0.814 and specificity of 0.707.Conclusion The levels of TyG,CAR and miR-21 in IHF pa-tients with T2DM and poor prognosis are increased,and the three are closely related to adverse cardiovascular events.Combined detection can be used as an auxiliary indicator for the prognosis of IHF patients with T2DM.