Student

Goh Yi Chong

Supervisor

Saw Shier Nee

Collaborator

Dr. Rahmah binti Saaid

In this era, accurate estimation of fetal weight is important in the management of labor and delivery. During the last decade, estimated fetal weight (EFW) has been incorporated into the standard routine antepartum evaluation of high-risk pregnancies and deliveries.

The main objective of this project is to develop machine learning models that can forecast EFW from the 28th gestational week onwards by using previous antenatal information of women with singleton pregnancies accurately.

Retrospective data of 347 patients in Singapore and 89397 scans in Malaysia, consisting of maternal demographics and ultrasound parameters collected between the 17th and 35th gestational weeks, were studied. Meanwhile, machine learning models were applied to different combinations of the features to forecast EFW from 28th gestational weeks onwards, in both Singapore and Malaysia dataset.