Predicting preterm birth with machine learning methods
Preterm birth is a leading cause for birth complications and neonatal mortality in the world. It remains difficult to predict whether a preterm birth will occur, which hinders the possible use of prevention treatments. This thesis investigates the use of machine learning models in the prediction of spontaneous preterm birth. In addition, possible heterogeneous performance of these models among dif
