Influential factors of mild cognitive impairment in type 2 diabetes mellitus based on machine learning and logistic regression
Objective To analyze the risk factors of mild cognitive impairment in type 2 diabetes mellitus(T2DM)by using machine learning and Logistic regression,and to provide basis for relevant intervention.Methods The data of 1 284 T2DM patients in the Department of Endocrinology of Nanjing Gulou Hospital were collected.The data were divided into the training set(70%)and the testing set(30%).Logistic regression model,Random Forest and XGBoost were used to construct the models,and the results were explained by the model interpretability.Results The random forest model had the highest predictive performance,the AUC value of the training set was 1.0 while the testing one was 0.783(95%CI:0.660-0.894),the model screened 19 important variables for MCI in patients with T2DM,such as education time,age,insulin sensitivity index,peripheral neuropathy,glycosylated hemoglobin,abnormal bone metabolism.Conclusions The prediction performance of random forest model is the best,which can help medical staff accurately identify the risk factors of MCI in patients with T2DM,and help medical staff to apply early intervention.