The early prediction of gestational diabetes mellitus by machine learning models.

Journal: BMC pregnancy and childbirth

PMID: 39217284

Abstract

BACKGROUND: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period.

Authors

Yeliz Kaya

Faculty of Health Sciences, Department of Gynecology and Obstetrics Nursing, Eskişehir Osmangazi University, Eskişehir, Turkey. yelizyilmazturk@gmail.com.
Zafer Bütün

Hoşnudiye Mah. Ayşen Sokak Dorya Rezidans, A Blok no:28/77, Eskişehir, Turkey.
Özer Çelik

Department of Mathematics and Computer, Faculty of Science and Letters, Eskişehir Osmangazi University, Eskişehir, Turkey.
Ece Akça Salik

Department of Gynecology and Obstetrics, Eskisehir City Hospital, Eskişehir, Turkey.
Tuğba Tahta

Ankara Medipol Üniversity, Health Services Vocational School, Ankara, Turkey.
Arzu Altun Yavuz

Faculty of Science, Department of Statistics, Eskişehir Osmangazi University, Eskisehir, Turkey.

Keywords

Adult Algorithms Blood Glucose Body Mass Index Diabetes, Gestational Female Glucose Tolerance Test Humans Machine Learning Parity Predictive Value of Tests Pregnancy Pregnancy Trimester, First Risk Factors Young Adult

External Resources

View on PubMed Access via DOI PubMed (39217284)

The early prediction of gestational diabetes mellitus by machine learning models.

Abstract

Authors

Keywords

External Resources

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