Analysing Factors for Improving Pregnancy Outcomes Using Machine Learning
Sarika Devi, Arpit Raj, Poonam S. Joshi, Sapna Rawat · 2023
Machine learning is a subset of artificial intelligence and one of the technical fields with the quickest growth. Classification and prediction (e.g. pandemic forecasting, medical diagnosis), clustering analysis (e.g. web security by identifying unusual traffic, cancer cell identification, customer segmentation), natural language processing (e.g. sentiment analysis, speech recognition) and a long list of other uses are some of the main applications of machine learning (ML). According to WHO, 810 women worldwide die every day as a result of complications during pregnancy and delivery. The vast majority (94%) of these deaths occur in low- and lower-middle-income countries. Recent technical developments have reduced the risk of maternal mortality, but it is still challenging to ensure the safety of both mother and fetus throughout pregnancy. The risks of pregnancy in this circumstance can be reduced by anticipating issues and implementing precautionary measures. This chapter&s;s primary objective is to assess contemporary approaches to research and development that centre on ML to forecast and identify various pregnancy problems. These viewpoints make use of ML approaches to forecast the ideal delivery method and pinpoint potential issues that may arise.