Comparing the Performance of Ensemble Methods in Predicting Emergency Department Admissions Using Machine Learning Techniques

Abstract

Healthcare data collection, storage, retrieval, and analysis are enabled by various technologies and tools in health information systems. These systems include health information exchanges, telemedicine platforms, clinical decision support systems, and electronic health records. They aim to improve patient outcomes, provider communication, and healthcare workflows. Machine learning is being used in emergency rooms to address challenges such as increasing patient volume, limited resources, and the need for quick decisions. Machine learning algorithms can assist in triage and risk stratification by identifying patients requiring urgent care and predicting the severity of their condition. By analyzing various patient data sources, machine learning can detect patterns and indicators that human clinicians may miss, enabling early intervention and potentially saving lives. However, there is a lack of comparative evaluation of ensemble methods used in analysis. Therefore, this study aims to thoroughly examine and analyze various ensemble methods to understand their efficacy and performance, contributing valuable insights to researchers and practitioners.

Keywords

• ensemble methods
• logistic regression
• prediction
• emergency department

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