Investigating Detection Strategy of Gestational Diabetes Mellitus During Pregnancy Using Machine Learning

Abstract

Artificial intelligence has been applied to numerous applications such as health, finance, social media, and online customer support systems. Machine learning (ML) is a subdivision of artificial intelligence and plays a vital role in health care prediction and diagnosis. It has been widely used to anticipate the mode of childbirth and evaluating the potential matriarchal hazards during pregnancy. This chapter aims to review the machine learning techniques to predict prenatal complications. Gestational diabetes mellitus (GDM) is a type of diabetes that develops during pregnancy. It is a condition in which the body is unable to produce enough insulin to meet the increased insulin needs of the mother and the developing fetus. This results in high blood sugar levels, which can cause complications for both the mother and the baby. This chapter explores the current research and development perspectives that utilize the ML techniques to anticipate the optimal mode of childbirth and to detect various complications during childbirth.