Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method
Abstract Gestational diabetes mellitus (GDM) is the most common disease in pregnancy and can cause a series of maternal and infant complications. A new study shows that GDM affects one in six deliveries. Identifying and screening for risk factors for GDM can effectively help intervene and improve th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2022-08-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-022-00110-8 |
| _version_ | 1828345817697615872 |
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| author | Xiaojia Wang Yurong Wang Shanshan Zhang Lushi Yao Sheng Xu |
| author_facet | Xiaojia Wang Yurong Wang Shanshan Zhang Lushi Yao Sheng Xu |
| author_sort | Xiaojia Wang |
| collection | DOAJ |
| description | Abstract Gestational diabetes mellitus (GDM) is the most common disease in pregnancy and can cause a series of maternal and infant complications. A new study shows that GDM affects one in six deliveries. Identifying and screening for risk factors for GDM can effectively help intervene and improve the condition of women and their children. Therefore, the aim of this paper is to determine the risk factors for GDM and to use the ensemble learning method to judge whether pregnant women suffer from GDM more accurately. First, this study involves six commonly used machine learning algorithms to analyze the GDM data from the Tianchi competition, selects the risk factors according to the ranking of each model, and uses the Shapley additive interpreter method to determine the importance of the selected risk factors. Second, the combined weighting method was used to analyze and evaluate the risk factors for gestational diabetes and to determine a group of important factors. Lastly, a new integrated light gradient-boosting machine-extreme gradient boosting-gradient boosting tree (LightGBM-Xgboost-GB) learning method is proposed to determine whether pregnant women have gestational diabetes mellitus. We used the gray correlation degree to calculate the weight and used a genetic algorithm for optimization. In terms of prediction accuracy and comprehensive effects, the final model is better than the commonly used machine learning model. The ensemble learning model is comprehensive and flexible and can be used to determine whether pregnant women suffer from GDM. In addition to disease prediction, the model can also be extended for use to many other areas of research. |
| first_indexed | 2024-04-14T00:17:53Z |
| format | Article |
| id | doaj.art-46a38d21ab7542f5b5c171f7e8248205 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-14T00:17:53Z |
| publishDate | 2022-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-46a38d21ab7542f5b5c171f7e82482052022-12-22T02:23:04ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832022-08-0115112010.1007/s44196-022-00110-8Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning MethodXiaojia Wang0Yurong Wang1Shanshan Zhang2Lushi Yao3Sheng Xu4Institute of Artificial Intelligence and Data Science, School of Management, Hefei University of TechnologyInstitute of Artificial Intelligence and Data Science, School of Management, Hefei University of TechnologyDepartment of Clinical Teaching, The First Affiliated Hospital of Anhui University of Chinese MedicineInstitute of Artificial Intelligence and Data Science, School of Management, Hefei University of TechnologyInstitute of Artificial Intelligence and Data Science, School of Management, Hefei University of TechnologyAbstract Gestational diabetes mellitus (GDM) is the most common disease in pregnancy and can cause a series of maternal and infant complications. A new study shows that GDM affects one in six deliveries. Identifying and screening for risk factors for GDM can effectively help intervene and improve the condition of women and their children. Therefore, the aim of this paper is to determine the risk factors for GDM and to use the ensemble learning method to judge whether pregnant women suffer from GDM more accurately. First, this study involves six commonly used machine learning algorithms to analyze the GDM data from the Tianchi competition, selects the risk factors according to the ranking of each model, and uses the Shapley additive interpreter method to determine the importance of the selected risk factors. Second, the combined weighting method was used to analyze and evaluate the risk factors for gestational diabetes and to determine a group of important factors. Lastly, a new integrated light gradient-boosting machine-extreme gradient boosting-gradient boosting tree (LightGBM-Xgboost-GB) learning method is proposed to determine whether pregnant women have gestational diabetes mellitus. We used the gray correlation degree to calculate the weight and used a genetic algorithm for optimization. In terms of prediction accuracy and comprehensive effects, the final model is better than the commonly used machine learning model. The ensemble learning model is comprehensive and flexible and can be used to determine whether pregnant women suffer from GDM. In addition to disease prediction, the model can also be extended for use to many other areas of research.https://doi.org/10.1007/s44196-022-00110-8Gestational diabetes mellitus (GDM)Machine learning modelEnsemble learning methodThe identification of risk factors for GDM |
| spellingShingle | Xiaojia Wang Yurong Wang Shanshan Zhang Lushi Yao Sheng Xu Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method International Journal of Computational Intelligence Systems Gestational diabetes mellitus (GDM) Machine learning model Ensemble learning method The identification of risk factors for GDM |
| title | Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method |
| title_full | Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method |
| title_fullStr | Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method |
| title_full_unstemmed | Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method |
| title_short | Analysis and Prediction of Gestational Diabetes Mellitus by the Ensemble Learning Method |
| title_sort | analysis and prediction of gestational diabetes mellitus by the ensemble learning method |
| topic | Gestational diabetes mellitus (GDM) Machine learning model Ensemble learning method The identification of risk factors for GDM |
| url | https://doi.org/10.1007/s44196-022-00110-8 |
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