Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm

Frontiers in Endocrinology

Endocrinology

Ya Yu¹  Jia Wang²  Xiaoqi Hu³  Xiaolin Hu⁴ 

[1] Department of Nursing, Guangzhou First People's Hospital, Guangzhou, Guangdong, China;Department of Nursing, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, China;Department of Nursing, Yantian District People's Hospital, Shenzhen, Guangdong, China;School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China;

关键词: gestational diabetes mellitus;    machine learning;    prediction model;    extreme gradient boosting;    logistic regression;   

DOI  :  10.3389/fendo.2023.1105062

 received in 2022-11-22, accepted in 2023-01-30,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveTo develop the extreme gradient boosting (XG Boost) machine learning (ML) model for predicting gestational diabetes mellitus (GDM) compared with a model using the traditional logistic regression (LR) method.MethodsA case–control study was carried out among pregnant women, who were assigned to either the training set (these women were recruited from August 2019 to November 2019) or the testing set (these women were recruited in August 2020). We applied the XG Boost ML model approach to identify the best set of predictors out of a set of 33 variables. The performance of the prediction model was determined by using the area under the receiver operating characteristic (ROC) curve (AUC) to assess discrimination, and the Hosmer–Lemeshow (HL) test and calibration plots to assess calibration. Decision curve analysis (DCA) was introduced to evaluate the clinical use of each of the models.ResultsA total of 735 and 190 pregnant women were included in the training and testing sets, respectively. The XG Boost ML model, which included 20 predictors, resulted in an AUC of 0.946 and yielded a predictive accuracy of 0.875, whereas the model using a traditional LR included four predictors and presented an AUC of 0.752 and yielded a predictive accuracy of 0.786. The HL test and calibration plots show that the two models have good calibration. DCA indicated that treating only those women whom the XG Boost ML model predicts are at risk of GDM confers a net benefit compared with treating all women or treating none.ConclusionsThe established model using XG Boost ML showed better predictive ability than the traditional LR model in terms of discrimination. The calibration performance of both models was good.

【 授权许可】

Unknown   
Copyright © 2023 Hu, Hu, Yu and Wang