Abstract Machine learning and medical diagnostic studies often struggle with the issue of
class imbalance in medical datasets, complicating accurate disease prediction and …

Sepsis is a life-threatening condition with high mortality rates and expensive treatment costs.
Early prediction of sepsis improves survival in septic patients. In this paper, we report our top …

Purpose Cardiac arrest (CA) is the most serious death-related event in critically ill patients
and the early detection of CA is beneficial to reduce mortality according to clinical research …

Objective Sepsis is a life-threatening condition in the ICU and requires treatment in time.
Despite the accuracy of existing sepsis prediction models, insufficient focus on reducing …

Early detection of sepsis can assist in clinical triage and decision-making, resulting in early
intervention with improved outcomes. This study aims to develop a machine learning …

Machine learning technology has a massive impact on society because it offers solutions to
solve many complicated problems like classification, clustering analysis, and predictions …

Sepsis is an inflammation caused by the body's systemic response to an infection. The
infection could be a result of many diseases, such as pneumonia, urinary tract infection, and …

Objectives Sepsis is a life-threatening condition which is responsible for a high proportion of
intra-hospital deaths and related healthcare costs each year. Early detection and treatment …

Background Imbalanced datasets pose significant challenges in predictive modeling,
leading to biased outcomes and reduced model reliability. This study addresses data …

Sepsis is one of the most challenging health conditions worldwide, with relatively high
incidence and mortality rates. It is shown that preventing sepsis is the key to avoid potentially …