Automated labour detection framework to monitor pregnant women with a high risk of premature labour using machine learning and deep learning

Abstract

It is estimated that more than 1 in 10 babies are born prematurely worldwide. Moreover, premature babies can face lifelong health-related disabilities, such as difficulties in learning or hearing and vision loss. By monitoring uterine contractions, physicians can evaluate the health and progress of the pregnancy and determine if the pregnant woman is in labour, thus assisting them to go to the hospital and help reduce the complications associated with premature birth. In this paper, we aim to mitigate the consequences of premature birth for pregnant women and the foetus using machine learning and deep learning approaches to detect and predict labour. The proposed system was tested for reliability and accuracy. The results show that the deep learning approach achieved the best results with a 0.98 accuracy rate.

Keywords

• Uterine contractions
• Labour
• Premature birth
• Machine learning
• Deep learning
• Artificial intelligence