Prediction of Preeclampsia in Pregnant Women Using Machine Learning Paradigm

Abstract

Machine learning is an area that helps to predict outcomes more accurately. It was utilized in different domains such as banking, healthcare, education, etc. Among all the domains, machine learning was largely utilized in the healthcare sector for predicting and diagnosing the disease in advance for saving millions of lives. ML has different kinds of algorithms which help to make the prediction process effective. This chapter focussed on explaining different machine learning algorithms for making better predictions in pregnancy complications in the healthcare domain. In general, there are different complications that women encountered during their pregnancy periods such as High BP, preeclampsia, anemia, etc. This work specifically aims to describe the preeclampsia complication during pregnancy. In machine learning, various kinds of regression algorithms are compared and analyzed. It also focused on which predictive technique would be more efficient for predicting the condition of preeclampsia in advance to save lives of pregnant women and also take necessary precautions.