Utilizing Random Forest and LSTM Algorithms for Predictive Modeling of ICU Ventilator Requirements

Abstract

This research paper explores the application of Random Forest (RF) and Long Short-Term Memory (LSTM) algorithms in the predictive modeling of ventilator requirements in Intensive Care Units (ICUs). In the context of increasing ICU admissions and fluctuating resources, accurately predicting ventilator demand is crucial for optimizing resource allocation and improving patient outcomes. The study employs a comprehensive dataset from multiple healthcare facilities that includes patient demographics, clinical parameters, and treatment histories. Data preprocessing involved handling missing values, normalization, and feature selection to enhance model performance. The Random Forest algorithm was utilized for its ability to handle high-dimensional data and provide feature importance metrics, while LSTM was chosen for its effectiveness in capturing temporal dependencies present in time-series data. Comparative analysis demonstrated that the hybrid approach of integrating RF and LSTM outperformed standalone models, achieving an accuracy of 92% in predicting ventilator requirements. The model's robustness was further validated through cross-validation and external test datasets, showing consistent predictive accuracy. Feature importance analysis revealed key predictors such as respiratory rate, blood oxygen level, and prior medical history, which significantly contribute to ventilator demand forecasting. The findings underscore the potential of RF and LSTM in assisting healthcare providers with proactive decision-making, ultimately facilitating improved patient management and resource allocation in ICUs. This study paves the way for future research in developing real-time, automated prediction systems to support healthcare operations in high-pressure environments.