Published online Oct 15, 2023. doi: 10.4239/wjd.v14.i10.1541
Peer-review started: August 1, 2023
First decision: August 16, 2023
Revised: August 21, 2023
Accepted: September 14, 2023
Article in press: September 14, 2023
Published online: October 15, 2023
Processing time: 69 Days and 10.6 Hours
Gestational diabetes mellitus (GDM) is a common metabolic disease during pregnancy, which has adverse effects on maternal and child health. The establishment and evaluation of risk prediction models can help to identify high-risk groups early and take corresponding intervention measures to reduce the risk in pregnant women and newborns. At present, research in this field mainly focuses on the screening of predictors and the construction of models and explores their reliability and practicability. These studies provide a theoretical basis and method support for the prevention and management of gestational diabetes.
The purpose of this study is to establish a reliable risk prediction model for gestational diabetes to help doctors detect and treat patients with GDM. The key issues to be solved in this study include determining the best predictors and establishing effective models. Solving these problems is of great significance for improving the diagnostic rate of early diabetes and reducing the risk of complications in pregnant women and fetuses. It will also have a positive effect on future research in this field.
The main objective of this study is to establish a reliable risk prediction model for GDM. The achieved goals include obtaining the risk factors of GDM, establishing a risk factor prediction model, and evaluating the model. The random forest model has a good prediction effect, which can effectively predict the risk of diabetes in pregnant women and indicate the direction for future research in this field.
In this study, a retrospective case analysis method was adopted, and the study subjects were stratified into two groups: Those with GDM and those without GDM. According to whether GDM occurred, the general data of the two groups of pregnant women were investigated and analyzed, and we established a risk prediction model for GDM during the trimester using both the logistic regression and random forest models, and the two models were evaluated and validated. The peculiarity and novelty of the research methods lie in the adoption of machine learning methods, which greatly improve the accuracy and reliability of the model.
This study successfully established a risk prediction model for early gestational diabetes in pregnant women (random forest and nomogram model). After analyzing and screening a number of clinical factors, the random forest model had high prediction accuracy and judgment ability. This study provides strong support for early prevention and intervention of gestational diabetes in pregnant women and provides a reference value for further research in this field. In the future, it is necessary to further expand the sample size, improve the considered factors and verify the stability and applicability of the model.
This study proposed a model for predicting the likelihood of developing gestational diabetes during the early stages of pregnancy and compared the predictive effects of the random forest and nomogram models. The results suggested that the random forest model can more accurately predict the risk of gestational diabetes during early pregnancy.
Future research should focus on improving the risk prediction model of gestational diabetes in pregnant women and improve the accuracy and stability of the model to meet clinical needs. We should also explore new predictors, explore pathological mechanisms, and identify intervention strategies to reduce the risk of diabetes and its complications in pregnant women and improve maternal health.