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Early Prediction of Gestational Diabetes Mellitus Using Machine Learning Approaches

Early Prediction of Gestational Diabetes Mellitus Using Machine Learning Approaches

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300078482
Enrollment
Unknown
Registered
2023-12-11
Start date
2023-12-11
Completion date
Unknown
Last updated
2023-12-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gestational Diabetes Mellitus

Interventions

Pregnant women who underwent prenatal assessments and delivered at our hospital from 2017 to 2018:None
Pregnant women who underwent prenatal assessments and delivered at our hospital or a branch of our hospital in 2023:None

Sponsors

Fujian Maternity and Child Health Hospital
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
18 Years to 48 Years

Inclusion criteria

Inclusion criteria: Pregnant women between the age of 18 to 48 who underwent prenatal assessments and delivered at our hospital or a branch of our hospital.

Exclusion criteria

Exclusion criteria: those with incomplete information.

Design outcomes

Primary

MeasureTime frame
Common GDM risk factors;Routine biochemical parameters;Routine blood indicators;Thyroid function indicators;—

Countries

China

Contacts

Public ContactMei Ma

Fujian Maternity and Child Health Hospital

mameifpmch@163.com+86 135 5943 4082

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026