A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management.

ABSTRACT:

Objective

This study aimed to establish a risk prediction model for diabetic retinopathy (DR) in the Chinese type 2 diabetes mellitus (T2DM) population using few inspection indicators and to propose suggestions for chronic disease management.

Methods

This multi-centered retrospective cross-sectional study was conducted among 2,385 patients with T2DM. The predictors of the training set were, respectively, screened by extreme gradient boosting (XGBoost), a random forest recursive feature elimination (RF-RFE) algorithm, a backpropagation neural network (BPNN), and a least absolute shrinkage selection operator (LASSO) model. Model I, a prediction model, was established through multivariable logistic regression analysis based on the predictors repeated ≥3 times in the four screen

SUBMITTER: Pan H 

PROVIDER: S-EPMC10172657 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature