Over the past few decades, artificial intelligence has been showing its high relevance and potential in a vast number of applications, particularly in the healthcare domain. Having a healthy pregnancy is one of the best ways to promote a healthy birth. Getting early and regular prenatal care improves the chances of a healthy pregnancy. Complications involved in the individual's pregnancy need to be predicted on time accurately. AI can help clinicians to make decisions by assisting them in decision-making. In this regard, the objective of this chapter is to provide a detailed survey of various pregnancy-related abnormalities; and to explore various machine learning algorithms to classify/predict pregnancy-related abnormalities with higher accuracy. A generic framework that focuses more on classifying various features into normal and abnormal, and to be monitored patients to provide support and care during an emergency.
Top1. Introduction
Maternal health consists of health of women during the entire period of pregnancy, childbirth, and after delivery. It includes the physical health, mental health, and social well-being of a woman throughout this entire reproductive cycle. Maternal health is crucial not only for the health and well-being of the mother but also for the survival and development of the child. Maternal health encompasses various aspects such as access to prenatal care, nutrition, safe and hygienic delivery, and postpartum care. It is essential to ensure that women have access to quality healthcare services during this period to prevent complications and ensure a safe and healthy pregnancy and childbirth experience (Kathuria, 2023). Various Figure 1 shows the various pregnancy abnormalities. There exist many types of pregnancy abnormalities that occur during pregnancy. Some of the most common pregnancy abnormalities include (Chen et al., 2023):
Miscarriage: A miscarriage is the loss of a pregnancy prior to the 20th week. It is usually caused by genetic abnormalities or problems with the uterus or cervix.
Ectopic pregnancy: When a fertilised egg imbeds outside of the uterus, particularly in the fallopian tube leads to ectopic pregnancy.
Gestational diabetes: Diabetes that develops during pregnancy. It causes increased blood sugar levels in the expectant mother and increases the fear of complications for both the maternal and infant.
Pre-eclampsia: Pre-eclampsia is a pregnancy-related disorder that often appears after the 20th week. It is life-threatening if left untreated and is characterised by hypertension and Proteinuria.
Placenta previa: Placenta previa, which causes bleeding during pregnancy and delivery, happens when the placenta completely or partially covers the cervix.
Preterm labor: when labor occurs prior to 37th week of pregnancy. This leads to premature birth and an increased risk of health problems for the baby.
Machine learning enables computers to continuously learn and enhance their performance on tasks without having to be explicitly programmed. It consists of assessing data and applying statistical models and algorithms to make predictions or decisions. The three main types of machine learning approach such as using labeled data, supervised learning, unlabelled data namely unsupervised and reinforcement learning. In learning through supervised approach, a set of labelled examples are provided to the computer, and it learns to identify patterns based on the labels. In unsupervised learning, a set of unlabeled data is given to the computer, and it uses the similarities and differences between the data points to recognise patterns. In reinforcement learning, the computer learns to make choices based on a reward system, where it receives rewards for choosing the correct decisions and penalized for choosing the wrong one. There exist a wide range of applications for machine learning in various domains such as image processing, speech signal processing, natural language processing, predictive modelling and recommendation systems. It is increasingly being used in fields such as healthcare, finance, and transportation to make more accurate predictions and improve decision-making (Akbulut et al., 2018).
Due to its capacity to analyse huge volumes of complicated data and offer insights that enhance patient outcomes in terms of diagnosis and treatment, machine learning (ML) has grown in significance in the healthcare industry. Here are a few ways in which ML is important in healthcare: Disease diagnosis: Machine learning algorithms are trained to analyze X-rays and MRIs, to detect abnormalities and identify potential diseases. This leads to earlier and more accurate diagnoses, which are crucial for patient outcomes.
Personalized treatment: ML is used to analyze patient data and predict how a patient will respond to a particular treatment. This helps doctors to customize treatments to individuals to reduce the side effects and for better outcomes.
Drug discovery: ML algorithms analyze large amounts of data on chemical compounds and their interactions with biological systems to identify potential new drugs. This helps speed up the drug discovery process and leads to the development of new treatments.