Early Prediction of Sepsis Using Ensembled Learning

Sepsis, a life-threatening condition stemming from the body's response to infection, requires timely intervention for improved patient outcomes. Recent decades have witnessed various studies focusing on creating various predictive methods for early sepsis prediction, however, the accuracy of their findings have not been at the utmost satisfaction for precise prediction of the early onset of sepsis. This study investigates sepsis prediction at the early stage using an ensembled learning approach, specifically employing machine learning approaches, Extreme Gradient Boosting (XGB) and Random Forest (RF). A comprehensive dataset encompassing clinical variables, vital signs, and laboratory measurements is utilized to train individual models and create an ensemble for enhanced predictive accuracy. Random Forest and Extreme Gradient Boosting models are trained on historical patient statistics to independently predict sepsis at the early stage. Subsequently, an ensemble is formed by combining the predictions of these two models, harnessing their complementary strengths. The ensembled learning approach employed stack ensemble machine learning approaches which demonstrates superior performance compared to individual models, thereby showcasing heightened sensitivity and specificity in early sepsis detection. Particularly, the combination of RF and XGB contributes to an improved ability to discern subtle patterns indicative of sepsis across diverse patient profiles. Analysis of feature importance was conducted to recognize the most significant variables in the ensembled model, providing valuable insights into the physiological indicators of impending sepsis. This research sheds light on the potential of ensembled learning, particularly when incorporating RF and XGB, in advancing early sepsis prediction models. The findings report a 99% accuracy thereby underscoring the significance of leveraging machine learning techniques to create robust clinical decision support systems, fostering more effective interventions and ultimately enhancing patient outcomes in critical care scenarios