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Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm
OBJECTIVE: To 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. METHODS: A case–control study was carried out among pregnant women, who were assi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10034315/ https://www.ncbi.nlm.nih.gov/pubmed/36967760 http://dx.doi.org/10.3389/fendo.2023.1105062 |
_version_ | 1784911189507047424 |
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author | Hu, Xiaoqi Hu, Xiaolin Yu, Ya Wang, Jia |
author_facet | Hu, Xiaoqi Hu, Xiaolin Yu, Ya Wang, Jia |
author_sort | Hu, Xiaoqi |
collection | PubMed |
description | OBJECTIVE: To 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. METHODS: A 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. RESULTS: A 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. CONCLUSIONS: The 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. |
format | Online Article Text |
id | pubmed-10034315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100343152023-03-24 Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm Hu, Xiaoqi Hu, Xiaolin Yu, Ya Wang, Jia Front Endocrinol (Lausanne) Endocrinology OBJECTIVE: To 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. METHODS: A 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. RESULTS: A 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. CONCLUSIONS: The 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. Frontiers Media S.A. 2023-03-09 /pmc/articles/PMC10034315/ /pubmed/36967760 http://dx.doi.org/10.3389/fendo.2023.1105062 Text en Copyright © 2023 Hu, Hu, Yu and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Endocrinology Hu, Xiaoqi Hu, Xiaolin Yu, Ya Wang, Jia Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm |
title | Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm |
title_full | Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm |
title_fullStr | Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm |
title_full_unstemmed | Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm |
title_short | Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm |
title_sort | prediction model for gestational diabetes mellitus using the xg boost machine learning algorithm |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10034315/ https://www.ncbi.nlm.nih.gov/pubmed/36967760 http://dx.doi.org/10.3389/fendo.2023.1105062 |
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