Predicting Pregnancy Complications Using Machine Learning

Abstract

The chapter starts out by going over the different kinds of pregnancy issues, their causes, and the difficulties in foreseeing them using conventional statistical techniques. The following section of the chapter gives a thorough summary of a number of research that employed machine learning methods to forecast pregnancy problems. In this chapter, the authors use a dataset of ultrasound images taken during pregnancy of different parts of the mother and the unborn child. After passing the dataset through pre-processing, where all the images were resized, the authors use data augmentation techniques to make the data more relevant for use in predictive modelling. To classify six classes, some pre-trained models were employed. The chapter ends with a review of the difficulties encountered by machine learning methods for anticipating pregnancy issues, such as the absence of extensive data sets and the requirement for additional validation studies.