aceDevelopment of machine learning models for fetal birth weight prediction: A single-center study in a Turkish population
Fetal birth weight (BW) is a critical indicator of the prognosis of perinatal health. Accurate estimation of BW is essential in determining the appropriate delivery method. This single-center study aims to evaluate the performance of various machine learning (ML) algorithms in estimating BW. The study population consisted of 456 pregnant women with singleton pregnancies evaluated at a single tertiary center in the Karaman region of Turkiye between 38 and 41 weeks based on their last menstrual period, who gave bi…
In a single-center study of 456 pregnant women in Turkiye, a linear regression model estimated fetal birth weight with a mean absolute error of 184.30 grams, against 223.33 grams for the Hadlock III formula.
The work is a single-center study in one Turkish population, and only women delivering a phenotypically normal newborn within 7 days of ultrasound were included, which limits how widely the results apply.
Sources
- Peer-reviewedAFRICAN JOURNAL OF REPRODUCTIVE HEALTH2026-11-01
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Truvace Impact Record TRV-2026-2426, v1: “Development of machine learning models for fetal birth weight prediction: A single-center study in a Turkish population.” Truvace, 2026-11-08. /record/TRV-2026-2426 (accessed on the day you copy it). sha256 8e98ebab01e1b1bc…
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