Early Prediction of Sepsis using Ensemble Learning

Sepsis is a fatal infection-related illness with a high mortality rate, especially among intensive care unit patients. Sepsis is a complex and varied illness driven by patient-specific factors such as immunological status, age, and comorbidities, as well as infection-related factors such as infection site and pathogen type. As a result, the contribution of these factors to organ damage differs between people, making sepsis a complicated and frequently lethal outcome of infection impacting several organ systems. Sepsis has a large global impact, with millions of cases and fatalities occurring each year, putting a significant strain on healthcare systems. The increased availability of healthcare data provides an opportunity for clinical decision support systems to improve patient outcomes. To forecast sepsis, this research presents a reference database and an ensemble approach classifier. This research aims to demonstrate the improvements made by machine-learning techniques by analyzing critical features and assessing the performance of different models, with the primary goal of improving sepsis results and advancing clinical decision support for this critical ailment.