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Evaluating Gestation Age Cutoff for Defining Premature Births in Africa: Mortality Prediction-Based Empiric Approach Using Data Science Computational Models Approach

Said Mohammed Ali from the Ministry of Health in Tanzania will use machine learning to develop a model for predicting gestational age based on routinely collected data and use it to better define when a birth should be classified as premature in Africa and thereby at higher risk of neonatal death. Premature births in Africa are currently defined as those occurring before 37 weeks. However, data suggests that births occurring just after this cut-off are also at higher risk of neonatal death, suggesting that the definition needs reevaluating. They will do this by developing an artificial neural network-based model using existing data, such as the date of the last menstrual period and pregnancy history, from 5,000 pregnancies with an ultrasound-verified gestational age to identify accurate predictors of gestational age. This will then be used to estimate gestational age on an additional 20,000 pregnancies, and further to identify the age cut-off that best predicts neonatal mortality, using logistic regression models.

More information about Data Science Approaches to Improve Maternal and Child Health in Africa

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