Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis
Abstract Background This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM). Methods A comprehensive and systematic search was conducted in Pubmed, Cochrane, Embase...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02848-x |
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| author | Meng Zhao Zhixin Yao Yan Zhang Lidan Ma Wenquan Pang Shuyin Ma Yijun Xu Lili Wei |
| author_facet | Meng Zhao Zhixin Yao Yan Zhang Lidan Ma Wenquan Pang Shuyin Ma Yijun Xu Lili Wei |
| author_sort | Meng Zhao |
| collection | DOAJ |
| description | Abstract Background This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM). Methods A comprehensive and systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science up to July 02, 2024. The quality of the studies included was assessed. The risk of bias was assessed through the prediction model risk of bias assessment tool and a graph was drawn accordingly. The meta-analysis was performed using Stata15.0. Results A total of 13 studies were included in the present review, involving 11,320 GDM patients and 22 ML models. The meta-analysis for ML models showed a pooled C-statistic of 0.82 (95% CI: 0.79 ~ 0.86), a pooled sensitivity of 0.76 (0.72 ~ 0.80), and a pooled specificity of 0.57 (0.50 ~ 0.65). Conclusion ML has favorable diagnostic accuracy for the progression of GDM to T2DM. This provides evidence for the development of predictive tools with broader applicability. |
| format | Article |
| id | doaj-art-e21aa8df010347eb85cb25d015e29e70 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-e21aa8df010347eb85cb25d015e29e702025-01-19T12:26:00ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125111010.1186/s12911-024-02848-xPredictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysisMeng Zhao0Zhixin Yao1Yan Zhang2Lidan Ma3Wenquan Pang4Shuyin Ma5Yijun Xu6Lili Wei7Department of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao UniversityDepartment of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao UniversityDepartment of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao UniversityDepartment of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao UniversityDepartment of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao UniversityDepartment of Emergency Pediatric, The Affiliated Hospital of Medical College Qingdao UniversityDepartment of Endocrinology and Metabolic Diseases, The Affiliated Hospital of Medical College Qingdao UniversityDepartment of Nursing, The Affiliated Hospital of Medical College Qingdao UniversityAbstract Background This systematic review aims to explore the early predictive value of machine learning (ML) models for the progression of gestational diabetes mellitus (GDM) to type 2 diabetes mellitus (T2DM). Methods A comprehensive and systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science up to July 02, 2024. The quality of the studies included was assessed. The risk of bias was assessed through the prediction model risk of bias assessment tool and a graph was drawn accordingly. The meta-analysis was performed using Stata15.0. Results A total of 13 studies were included in the present review, involving 11,320 GDM patients and 22 ML models. The meta-analysis for ML models showed a pooled C-statistic of 0.82 (95% CI: 0.79 ~ 0.86), a pooled sensitivity of 0.76 (0.72 ~ 0.80), and a pooled specificity of 0.57 (0.50 ~ 0.65). Conclusion ML has favorable diagnostic accuracy for the progression of GDM to T2DM. This provides evidence for the development of predictive tools with broader applicability.https://doi.org/10.1186/s12911-024-02848-xClinical prediction modelGestational diabetes mellitusMachine learningType 2 diabetes mellitusSystematic review |
| spellingShingle | Meng Zhao Zhixin Yao Yan Zhang Lidan Ma Wenquan Pang Shuyin Ma Yijun Xu Lili Wei Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis BMC Medical Informatics and Decision Making Clinical prediction model Gestational diabetes mellitus Machine learning Type 2 diabetes mellitus Systematic review |
| title | Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis |
| title_full | Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis |
| title_fullStr | Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis |
| title_full_unstemmed | Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis |
| title_short | Predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes: a systematic review and meta-analysis |
| title_sort | predictive value of machine learning for the progression of gestational diabetes mellitus to type 2 diabetes a systematic review and meta analysis |
| topic | Clinical prediction model Gestational diabetes mellitus Machine learning Type 2 diabetes mellitus Systematic review |
| url | https://doi.org/10.1186/s12911-024-02848-x |
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