PEFNN: Parallel Evolving Fuzzy Neural Network for Sepsis Identification in Patients

In the healthcare domain, the fusion of machine learning with medical examination data has markedly propelled the monitoring of sepsis. This perilous medical condition is currently subject to heightened scrutiny owing to its profound implications for patient well-being. This paper introduces the Parallel Evolving Fuzzy Neural Network Approach (PEFNN), highlighting its interpretability and effectiveness in producing classification outcomes, attaining an accuracy of approximately 93%, with explicit interpretability of the results. The application of PEFNN underscores the paramount significance of timely sepsis recognition for facilitating effective medical intervention.