Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity
AimWe aimed to develop and internally validate a machine learning (ML)-based model for the prediction of the risk of type 2 diabetes mellitus (T2DM) in children with obesity.MethodsIn total, 292 children with obesity and T2DM were enrolled between July 2023 and February 2024 and followed for at leas...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1649988/full |
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| author | Jin-Xia Yang Jin-Xia Yang Yue Liu Yue Liu Rong Huang Hai-ying Wu Ya-yun Wang Su-ying Cao Guo-ying Wang Jian-Min Zhang Zi-Sheng Ai Hui-min Zhou |
| author_facet | Jin-Xia Yang Jin-Xia Yang Yue Liu Yue Liu Rong Huang Hai-ying Wu Ya-yun Wang Su-ying Cao Guo-ying Wang Jian-Min Zhang Zi-Sheng Ai Hui-min Zhou |
| author_sort | Jin-Xia Yang |
| collection | DOAJ |
| description | AimWe aimed to develop and internally validate a machine learning (ML)-based model for the prediction of the risk of type 2 diabetes mellitus (T2DM) in children with obesity.MethodsIn total, 292 children with obesity and T2DM were enrolled between July 2023 and February 2024 and followed for at least 1 year. Eight ML algorithms (Decision Tree, Logistic Regression, Support Vector Machine (SVM), Multilayer Perceptron, Adaptive Boosting, Random Forest, Gradient Boosting Decision Tree, and Extreme Gradient Boosting) were compared for their capacity to identify key clinical and laboratory characteristics of T2DM in children and to create a risk prediction model.ResultsForty-nine children were diagnosed with T2DM during the follow-up period. The SVM algorithm was the best predictor of T2DM, with the largest area under the receiver operating characteristic curve (0.98) and accuracy (93.2%). The SVM algorithm identified eight predictors: BMI, creatinine, prealbumin, glucose (180 min), glycosylated hemoglobin A1c, thyrotropin, total thyroxine (T4), and free T4 concentrations. Thus, an ML-based prediction model accurately identifies children with obesity at high risk of T2DM. If externally validated, this tool could facilitate early, personalized interventions aimed at preventing T2DM.DiscussionThe rising prevalence of obesity in childhood is associated with an increase in the risk of early-onset T2DM. Therefore, the early identification of individuals at high risk is crucial to prevent the development of this disease. In a comparative analysis of the performance of multiple ML algorithms, we found that the SVM algorithm was the best predictor of the development of T2DM. |
| format | Article |
| id | doaj-art-7779208f01764ad8b4e802dc2c4195d0 |
| institution | Kabale University |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-7779208f01764ad8b4e802dc2c4195d02025-08-20T03:41:08ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-08-011610.3389/fendo.2025.16499881649988Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesityJin-Xia Yang0Jin-Xia Yang1Yue Liu2Yue Liu3Rong Huang4Hai-ying Wu5Ya-yun Wang6Su-ying Cao7Guo-ying Wang8Jian-Min Zhang9Zi-Sheng Ai10Hui-min Zhou11School of Medicine, Tongji University, Basic Medical Science, Shanghai, ChinaChildren’s Hospital of Soochow University, Endocrine Genetic Metabolism, Suzhou, ChinaSchool of Medicine, Tongji University, Basic Medical Science, Shanghai, ChinaGongli Hospital, Pudong New District, Department of Orthopedic Surgery, Shanghai, ChinaShanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, ChinaChildren’s Hospital of Soochow University, Endocrine Genetic Metabolism, Suzhou, ChinaChildren’s Hospital of Soochow University, Endocrine Genetic Metabolism, Suzhou, ChinaChildren’s Hospital of Soochow University, Endocrine Genetic Metabolism, Suzhou, ChinaChildren’s Hospital of Soochow University, Endocrine Genetic Metabolism, Suzhou, ChinaChildren’s Hospital of Soochow University, Department of Traditional Chinese Medical, Suzhou, ChinaSchool of Medicine, Tongji University, Basic Medical Science, Shanghai, ChinaChildren’s Hospital of Soochow University, Endocrine Genetic Metabolism, Suzhou, ChinaAimWe aimed to develop and internally validate a machine learning (ML)-based model for the prediction of the risk of type 2 diabetes mellitus (T2DM) in children with obesity.MethodsIn total, 292 children with obesity and T2DM were enrolled between July 2023 and February 2024 and followed for at least 1 year. Eight ML algorithms (Decision Tree, Logistic Regression, Support Vector Machine (SVM), Multilayer Perceptron, Adaptive Boosting, Random Forest, Gradient Boosting Decision Tree, and Extreme Gradient Boosting) were compared for their capacity to identify key clinical and laboratory characteristics of T2DM in children and to create a risk prediction model.ResultsForty-nine children were diagnosed with T2DM during the follow-up period. The SVM algorithm was the best predictor of T2DM, with the largest area under the receiver operating characteristic curve (0.98) and accuracy (93.2%). The SVM algorithm identified eight predictors: BMI, creatinine, prealbumin, glucose (180 min), glycosylated hemoglobin A1c, thyrotropin, total thyroxine (T4), and free T4 concentrations. Thus, an ML-based prediction model accurately identifies children with obesity at high risk of T2DM. If externally validated, this tool could facilitate early, personalized interventions aimed at preventing T2DM.DiscussionThe rising prevalence of obesity in childhood is associated with an increase in the risk of early-onset T2DM. Therefore, the early identification of individuals at high risk is crucial to prevent the development of this disease. In a comparative analysis of the performance of multiple ML algorithms, we found that the SVM algorithm was the best predictor of the development of T2DM.https://www.frontiersin.org/articles/10.3389/fendo.2025.1649988/fullobesitychildrenrisk prediction modelmachine learningtype 2 diabetes mellitus |
| spellingShingle | Jin-Xia Yang Jin-Xia Yang Yue Liu Yue Liu Rong Huang Hai-ying Wu Ya-yun Wang Su-ying Cao Guo-ying Wang Jian-Min Zhang Zi-Sheng Ai Hui-min Zhou Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity Frontiers in Endocrinology obesity children risk prediction model machine learning type 2 diabetes mellitus |
| title | Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity |
| title_full | Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity |
| title_fullStr | Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity |
| title_full_unstemmed | Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity |
| title_short | Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity |
| title_sort | development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity |
| topic | obesity children risk prediction model machine learning type 2 diabetes mellitus |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1649988/full |
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