A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes.

ABSTRACT:

Background

Accurate classification of type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in the early phase is crucial for individual precision treatment. This study aimed to develop a classification model having fewer and easier to access clinical variables to distinguish T1DM in newly diagnosed diabetes in adults.

Methods

Clinical and laboratory data were collected from 15,206 adults with newly diagnosed diabetes in this cross-sectional study. This cohort represented 20 provinces and 4 municipalities in China. Types of diabetes were determined based on postprandial C-peptide (PCP) level and glutamic acid decarboxylase autoantibody (GADA) titer. We developed multivariable clinical diagnostic models using the eXtreme Gradient Boosting (XGBoost) algorithm. Classification varia

SUBMITTER: Tang X 

PROVIDER: S-EPMC8033361 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature