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

Journal: Journal of diabetes research

PMID: 32626780

Abstract

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 conventional regression.

Authors

Yunzhen Ye

Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
Yu Xiong

Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Qiongjie Zhou

Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
Jiangnan Wu

Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
Xiaotian Li

Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
Xirong Xiao

Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.

Keywords

Adult Blood Glucose Cholesterol Clinical Decision Rules Cohort Studies Decision Trees Diabetes, Gestational Female Glycated Hemoglobin Humans Lipoproteins, HDL Logistic Models Machine Learning Maternal Age Pregnancy Pregnancy in Obesity Reproducibility of Results Retrospective Studies Routinely Collected Health Data Triglycerides Uric Acid Young Adult

External Resources

View on PubMed Access via DOI PubMed (32626780)

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

Abstract

Authors

Keywords

External Resources

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