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Hybrid Optimization of Recurrent Neural Networks for Accurate Prediction of ICU Patient Mortality Rates.

Sam M. K.
Volume 5

Abstract

Accurate prediction of mortality rates among Intensive Care Unit (ICU) patients is a critical challenge in modern clinical medicine, as timely risk stratification enables physicians to prioritize interventions, allocate limited resources effectively, and improve patient survival outcomes. Despite significant advances in machine learning-based prognostic models, existing approaches often suffer from limited generalization, sensitivity to missing clinical data, and insufficient exploitation of temporal dependencies inherent in physiological time-series records. This paper proposes a hybrid optimization framework that integrates Recurrent Neural Networks (RNNs) — specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) architectures — with metaheuristic optimization algorithms, including Particle Swarm Optimization (PSO) and the Grey Wolf Optimizer (GWO), to enhance the predictive accuracy and convergence efficiency of ICU mortality models. The proposed framework was trained and validated on the publicly available MIMIC-III clinical database, encompassing over 53,000 ICU admissions with multivariate time-series features including vital signs, laboratory results, demographic data, and clinical severity scores (SOFA and APACHE II). A comprehensive preprocessing pipeline was applied to handle missing values, normalize temporal features, and address severe class imbalance through synthetic minority oversampling (SMOTE). The hybrid optimization mechanism was employed to automatically tune critical hyperparameters — including learning rate, number of hidden layers, dropout rates, and batch size — replacing conventional manual tuning and grid search strategies. Experimental evaluations demonstrate that the proposed hybrid model achieves an AUC-ROC of 0.967, a sensitivity of 94.3%, a specificity of 95.8%, and an F1-score of 0.943, significantly outperforming standalone LSTM, standard RNN, and traditional machine learning baselines such as Random Forest and XGBoost. Furthermore, SHAP (SHapley Additive exPlanations) analysis was incorporated to identify and rank the most influential clinical predictors of ICU mortality, providing transparent and clinically interpretable insights to support physician decision-making. The proposed framework demonstrates robust performance across diverse ICU subpopulations — including cardiac, respiratory, and surgical patients — underscoring its potential as a generalizable and deployable clinical decision support tool in real-world intensive care settings.

 

Keywords

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