Comparison of Machine Learning Methods and Conventional Logistic Regressions for Predicting Gestational Diabetes Using Routine Clinical Data: A Retrospective Cohort Study

BACKGROUND: Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible prediction algorithms with potential advantages over co...

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Detalles Bibliográficos
Autores principales: Ye, Yunzhen, Xiong, Yu, Zhou, Qiongjie, Wu, Jiangnan, Li, Xiaotian, Xiao, Xirong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306091/
https://www.ncbi.nlm.nih.gov/pubmed/32626780
http://dx.doi.org/10.1155/2020/4168340

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306091/
https://www.ncbi.nlm.nih.gov/pubmed/32626780
http://dx.doi.org/10.1155/2020/4168340

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