Bio inspired feature selection and graph learning for sepsis risk stratification

Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accurate risk stratification. However, existing machine learning models for sepsis prediction often suffer from poor interpretability, limited generalizability across diverse patient populations, and challenges in handling class imbalance and high-dimensional clinical data. To address these gaps, this study proposes a novel framework that integrates bio-inspired feature selection and graph-based deep learning for enhanced sepsis risk prediction. Using the MIMIC-IV dataset, we employ the Wolverine Optimization Algorithm (WoOA) to select clinically relevant features, followed by a Generative Pre-Training Graph Neural Network (GPT-GNN) that models complex patient relationships through self-supervised learning. To further improve predictive accuracy, the TOTO metaheuristic algorithm is applied for model fine-tuning. SMOTE is used to balance the dataset and mitigate bias toward the majority class. Experimental results show that our model outperforms traditional classifiers such as SVM, XGBoost, and LightGBM in terms of accuracy, AUC, and F1-score, while also providing interpretable mortality indicators. This research contributes a scalable and high-performing decision support tool for sepsis risk stratification in real-world clinical environments.