A Predictive Model for Diabetic Retinopathy Based on Ensemble Learning

Abstract

Abstract Objective The purpose of this passage is to predict the risk of type 2 daibetes complicated with retinopathy，we evaluated 14 commonly used models and fusion them in a glm stacking classifier. Methods The Clinical data of this passage comes from National Health Science Data Center (Diabetic complications early warning dataset), all the statistical analysis were finished in R-4.2.1, Rstudio. We used recursive feature elimination to get variables we need, we create models in caret package, and computed Accuracy, Precision, Sensitivity, Specificity, F1-score of every models,choose the better models to the stacking classifier. Results REF feature screening shows that the accuracy of the models improve with the number of variables, and tends to be flat after more than 30 variables, in order to prevent overfitting, combined with the literature, a total of 45 variables are selected into the model, and the evaluation indicators show that the support vector machine, AdaBoost, XGBoost, rotating forest, are excellent in the first-stage modeling. The fusion of stacking models of generalized linear models is better than stage one models. Conclusion The stacking fusion model can improve the performance of the model on the basis of a single model, and can play a certain role in the screening and prediction of high-risk groups with type 2 diabetes complicated by retinopathy in the clinic.