Machine Learning based Postpartum Hemorrhage Prediction Model

Postpartum hemorrhage (PPH) is an obstetric emergency instigated by excessive blood loss which occurs frequently after the delivery. The PPH can result in volume depletion, hypovolemic shock, and anemia. This is particular condition is considered a major cause of maternal deaths around the globe. Presently, physicians utilize visual examination for calculating blood and flfluid loss during delivery. Since the classical methods depend on expert knowledge and are inaccurate, automated machine learning based PPH diagnosis models are essential. In regard to this aspect, this study introduces an effiffifficient oppositional binary crow search algorithm (OBCSA) with an optimal stacked auto encoder (OSAE) model, called OBCSA-OSAE for PPH prediction. The goal of the proposed OBCSA-OSAE technique is to detect and classify the presence or absence of PPH. The OBCSA-OSAE technique involves the design of OBCSA based feature selection (FS) methods to elect an optimum feature subset. Additionally, the OSAE based classifification model is developed to include an effffective parameter adjustment process utilizing Equilibrium Optimizer (EO). The performance validation of the OBCSA-OSAE technique is performed using the benchmark dataset. The experimental values pointed out the benefifits of the OBCSA-OSAE approach in recent methods.