Study on risk factors of impaired fasting glucose and development of a prediction model based on Extreme Gradient Boosting algorithm.

ABSTRACT:

Objective

The aim of this study was to develop and validate a machine learning-based model to predict the development of impaired fasting glucose (IFG) in middle-aged and older elderly people over a 5-year period using data from a cohort study.

Methods

This study was a retrospective cohort study. The study population was 1855 participants who underwent consecutive physical examinations at the First Affiliated Hospital of Soochow University between 2018 and 2022.The dataset included medical history, physical examination, and biochemical index test results. The cohort was randomly divided into a training dataset and a validation dataset in a ratio of 8:2. The machine learning algorithms used in this study include Extreme Gradient Boosting (XGBoost), Support Vector Machines (SV

SUBMITTER: Cui Q 

PROVIDER: S-EPMC11458394 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature