Optimizing sepsis mortality prediction using hybrid federated learning and explainable AI framework.

Sepsis is a life-threatening condition resulting from a dysregulated host response to infection, frequently leading to organ failure and high mortality in hospital settings. Early identification of sepsis is critical for reducing mortality; however, conventional diagnostic approaches often fail to capture complex clinical patterns at an early stage. Recent advances in machine learning (ML) and explainable artificial intelligence (XAI) have demonstrated potential for improving predictive accuracy while supporting clinical interpretability. Nevertheless, concerns related to data privacy and model transparency continue to limit real-world clinical adoption. To address these challenges, this study proposes a hybrid framework that integrates federated learning with ensemble-based machine learning models and explainable AI techniques for sepsis mortality prediction. The framework employs Random Forest, LightGBM, XGBoost, K-Nearest Neighbors, and Logistic Regression models, trained in a decentralized manner to preserve patient data privacy. Model interpretability is enhanced using SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Partial Dependence Plots (PDP), enabling transparent and clinician-oriented decision support. The proposed framework is evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (ROC–AUC), in both centralized and federated settings. Experimental results demonstrate that ensemble models, particularly Random Forest and gradient boosting methods, achieve high predictive performance while maintaining robustness in a federated environment. The findings indicate that combining FL with XAI enables accurate, privacy-preserving, and interpretable sepsis mortality prediction, supporting reliable clinical decision-making and potential deployment in real-time intensive care unit applications.