An IoT-Based Pregnancy Complexity Identification Using Machine Learning

Abstract

Pregnancy complications have the potential to seriously injure both the mother and the developing baby, increasing the risk of morbidity and mortality. Early detection of high-risk pregnancies is crucial to reducing the likelihood of such issues and improving the health outcomes for mothers and babies. The authors suggest an IoT-based system that uses machine learning to recognise pregnancy complications as a solution to this problem. The system uses a variety of sensors to collect information from pregnant women, including blood pressure, heart rate, foetal heart rate, and temperature sensors. This collected data is then analyzed using machine learning algorithms using supervised learning algorithms for classification and regression analysis. The study's findings indicate that the suggested system can effectively recognize pregnancy complications with high levels of accuracy, sensitivity, specificity, and AUC. The system holds great potential in enhancing maternal and fetal health outcomes by enabling the early detection and intervention of high-risk pregnancies.