Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1105062/full |
| _version_ | 1827996417880227840 |
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| author | Xiaoqi Hu Xiaolin Hu Ya Yu Jia Wang |
| author_facet | Xiaoqi Hu Xiaolin Hu Ya Yu Jia Wang |
| author_sort | Xiaoqi Hu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-10T05:11:54Z |
| format | Article |
| id | doaj.art-bcca8970ad4f47f4902fa9db50e4e58e |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-10T05:11:54Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-bcca8970ad4f47f4902fa9db50e4e58e2023-03-09T07:17:32ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-03-011410.3389/fendo.2023.11050621105062Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithmXiaoqi Hu0Xiaolin Hu1Ya Yu2Jia Wang3Department of Nursing, Yantian District People's Hospital, Shenzhen, Guangdong, ChinaSchool of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, ChinaDepartment of Nursing, Guangzhou First People's Hospital, Guangzhou, Guangdong, ChinaDepartment of Nursing, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, ChinaObjectiveTo 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.https://www.frontiersin.org/articles/10.3389/fendo.2023.1105062/fullgestational diabetes mellitusmachine learningprediction modelextreme gradient boostinglogistic regression |
| spellingShingle | Xiaoqi Hu Xiaolin Hu Ya Yu Jia Wang Prediction model for gestational diabetes mellitus using the XG Boost machine learning algorithm Frontiers in Endocrinology gestational diabetes mellitus machine learning prediction model extreme gradient boosting logistic regression |
| 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 | gestational diabetes mellitus machine learning prediction model extreme gradient boosting logistic regression |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1105062/full |
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