Construction and verification of prediction model of type 2 diabetic nephropathy based on machine learning
10.11958/20231584
- VernacularTitle:基于机器学习对2型糖尿病肾病预测模型的构建及验证
- Author:
Xian WANG
1
;
Xiaming LIU
;
Manyu CHEN
;
Jun ZHAO
;
Lidong WANG
Author Information
1. 承德医学院研究生院(邮编 067000)
- Keywords:
diabetes mellitus,type 2;
diabetic nephropathy;
machine learning;
monocytes;
neural networks,computer;
prediction model
- From:
Tianjin Medical Journal
2024;52(7):775-780
- CountryChina
- Language:Chinese
-
Abstract:
Objective To search for independent predictive factors of diabetic kidney disease(DKD)in patients with type 2 diabetes mellitus(T2DM),construct and validate an optional machine learning(ML)model for the risk of DKD.Methods A total of 528 patients with T2DM,hospitalized in the Endocrinology Department of Chengde Central Hospital from October 2019 to September 2020,were selected as the study objects,and patients were randomly divided into a training set(370 cases),and a validation set(158 cases).The training set was divided into the DKD group(89 cases)and the non-DKD group(281 cases)according to whether DKD existed.The general data and diagnostic examination of patients were performed by univariate analysis,in which variables with statistical differences were used to screen the best predictors by least absolute shrinkage and selection operator(LASSO)regression analysis.The best predictors were used to establish eight ML algorithms by three cross-validation methods,including Logistic regression(LR),K-nearest neighbor(KNN),support vector machine(SVM),decision tree(DT),random forest(RF),naive Bayes(NB),artificial neural network(ANN),and extreme gradient lift(XGBoost).The optimal prediction model was selected by receiver operating characteristic(ROC)curve,Delong test and GiViTI calibration curve.Decision curve analysis(DCA)was used to evaluate the clinical practicability of the model.Results Age,alanine aminotransferase,creatinine,triglyceride,cystatin C,25-hydroxy vitamin D and monocyte count were independent predictive factors of DKD.Eight ML models were established based on the above 7 predictors,and the ANN model performed best in the 8 ML models.The GiViTiI calibration curve indicated that the model had good accuracy(P＞0.05),and the DCA showed that the prediction model curve had clinical practical value in the threshold probability range of 0.027-0.612.Conclusion In this study,the ANN model constructed in this study to predict the risk of DKD is helpful for early discrimination of high-risk T2DM patients with DKD.
