Abstract

Info

Author(s):: SHEN Siyuan; LUO Dongmei; School of Mathematics and Physics, Anhui University of Technology, Maanshan 243002, China

Keywords:: Keywords: diabetic retinopathy mutual information combination model Stacking method risk prediction

Abstract:: Abstract: Objective To construct combination models for the risk prediction of diabetic retinopathy (DR), thereby providing a reference for the prevention and diagnosis of DR. Methods Based on the biochemical test data of 3 000 diabetic patients, the characteristic factors related to DR which were screened out by taking mutual information as the evaluation criterion were input as the modeling variables to construct 5 common models. According to the evaluation criteria of accuracy rate, precision rate, recall rate and AUC, 3 models with the superior prediction ability were screened out for model combination by Stacking method. Results A total of 39 characteristic factors were screened out through mutual information. The prediction performances of random forest model, SVM model and Logistic regression model were better. Among 3 combination models constructed, it was found that the combination model with SVM and Logistic as the primary classifier and random forest as the secondary classifier had the optimal prediction ability, and its AUC was as high as 0.877. Conclusion The combination model is superior to the single model in predicting DR risk, and is helpful for the clinical diagnosis of DR.