Machine Learning Techniques for Predicting Pregnancy Complications

Machine learning is employed extensively in healthcare, prediction, diagnosis, and as a technique of establishing priority. Artificial intelligence is widely used in the medical industry. There are a variety of tools in the disciplines of obstetrics and childcare that use machine learning techniques. The goal of the current chapter is to examine current research and development views that employ machine learning approaches to identify different complications during delivery. The common complications such as gestational diabetes mellitus, preeclampsia, stillbirth, depression and anxiety, preterm labor, high blood pressure, miscarriage were explored in this chapter. It investigated a synthesized picture of the features utilized, the types of features, the data sources, and its characteristics; it analyzed the adopted machine learning algorithms and their performances; and it gave a summary of the features employed. Eventually, the results of this review research helped to create a conceptual framework for improving the maternal healthcare system based on machine learning.