A stacked ensemble deep learning model for predicting the intensive care unit patient mortality

Abstract

Accurate mortality prediction in intensive care units (ICUs) is essential for optimizing patient treatment, nursing care, and resource allocation. Traditional models, such as Acute Physiology and Chronic Health Evaluation and Simplified Acute Physiology Score, have been very important in clinical practice, but they frequently have issues with prediction accuracy and adaptability, especially when dealing with complex and evolving patient data. These issues can be resolved, and the accuracy of mortality prediction increased due to recent developments in machine learning, especially deep learning. The present study introduces a new deep learning ensemble model that achieves a significant improvement over existing methods. Using stacked ensemble learning, our approach combines the advantages of one Random Forests model and two CatBoost models. We achieved a notable performance in mortality prediction by carefully training and optimizing this ensemble using the electronic ICU Collaborative Research Database. Our model boasts an accuracy of 94.19%, precision of 94.097%, recall of 94.29%, and F1-score of 94.191%, demonstrating a substantial improvement over conventional approaches. The prediction of ICU mortality has been significantly improved using ensemble learning, which helps medical and nursing staff to better treat patients individually, allocate resources efficiently, and enhance patient outcomes. This approach gives healthcare experts the ability to make data-driven decisions, leading to more effective and efficient care within the ICU.