Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults.

ABSTRACT: Purpose: We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults. Methods: A 3-year cohort study was performed on 15,928 Chinese adults without diabetes at baseline. All participants were randomly divided into a training set (n = 7,940) and a validation set (n = 7,988). XGBoost method is an effective machine learning technique used to select the most important variables from candidate variables. And we further established a stepwise model based on the predictors chosen by the XGBoost model. The area under the receiver operating characteristic curve (AUC), decision curve and calibration analysis were used to assess discrimination, clinical use an

SUBMITTER: Wu Y 

PROVIDER: S-EPMC8275929 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature