Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor
Objective To investigate the performance of the machine learning (ML) model in predicting small-for-gestational-age (SGA) at birth, using second-trimester data. Methods Retrospective data of 347 patients, consisting of maternal demographics and ultrasound parameters collected between the 20th and 25...
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| Main Authors: | , , , , |
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| Format: | Article |
| Published: |
Wiley
2021
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/27997/ |
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