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.
TopIntroduction
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. GDM typically develops around the 24th to 28th week of gestation and affects about 2-10% of pregnancies. Risk factors for GDM include a family history of diabetes, being overweight or obese before pregnancy, being over the age of 25, having previously given birth to a baby weighing over 9 pounds, and having polycystic ovary syndrome (PCOS).Complications of GDM can include high blood pressure, pre-eclampsia, the need for a cesarean section, premature birth, low blood sugar (hypoglycemia) after birth, and being large for gestational age, which can lead to difficulties during delivery.
GDM is a significant public health concern affecting pregnant women worldwide. According to the World Health Organization (WHO), GDM is defined as “any degree of glucose intolerance with onset or first recognition during pregnancy” (WHO, 2013). The global prevalence of GDM is estimated to be 14% and is increasing in many countries due to rising rates of obesity and other risk factors. The incidence of GDM is also higher among certain ethnic groups, such as South Asians, East Asians, and Pacific Islanders (Metzger & Coustan, 2013).
Complications of GDM can include macrosomia (large birth weight), pre-eclampsia, shoulder dystocia, neonatal hypoglycemia, and increased risk of type 2 diabetes and cardiovascular disease in both the mother and child (HAPO Study Cooperative Research Group, 2008). The diagnosis of GDM is typically made using a glucose tolerance test, which involves drinking a glucose solution and measuring blood sugar levels. The WHO recommends a two-step approach for diagnosing GDM, with a screening test followed by a diagnostic test if the screening test is positive (WHO, 2013). Management of GDM involves a multidisciplinary approach, with regular monitoring of blood sugar levels, dietary changes, physical activity, and in some cases, medication such as insulin. The goal of treatment is to maintain blood sugar levels within a target range to minimize the risk of complications for both the mother and baby (American Diabetes Association, 2021).
In recent years, there have been debates about the optimal diagnostic criteria and treatment targets for GDM. The International Association of Diabetes and Pregnancy Study Groups (IADPSG) recommended more stringent diagnostic criteria for GDM in 2010, which were later adopted by the WHO in 2013. However, some studies have questioned the benefits of these stricter criteria, particularly in terms of the increased cost and potential overtreatment of women who may not have developed GDM using previous criteria (Hartling et al., 2019).