Value of machine learning algorithms for predicting diabetes risk: A subset analysis from a real-world retrospective cohort study.

ABSTRACT:

Aims/introduction

To compare the application value of different machine learning (ML) algorithms for diabetes risk prediction.

Materials and methods

This is a 3-year retrospective cohort study with a total of 3,687 participants being included in the data analysis. Modeling variable screening and predictive model building were carried out using logistic regression (LR) analysis and 10-fold cross-validation, respectively. In total, six different ML algorithms, including random forests, light gradient boosting machine, extreme gradient boosting, adaptive boosting (AdaBoost), multi-layer perceptrons and gaussian naive bayes were used for model construction. Model performance was mainly evaluated by the area under the receiver operating characteristic curve. The best performing M

SUBMITTER: Mao Y 

PROVIDER: S-EPMC9889616 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature