Age-Based Predictive Modeling of Emergency Department Visits Using Machine Learning

Emergency department (ED) overcrowding, and re-source optimization are significant challenges healthcare systems face globally. Accurate forecasting of ED visits plays a pivotal role in proactive resource allocation and efficient patient management. This study explores the application of the Support Vector Machine (SVM) model to predict Emergency Department (ED) visits stratified by age groups. VMs are chosen for their ability to handle non-linear relationships and high-dimensional data, making them well-suited for complex healthcare prediction tasks. The goal is to evaluate the effectiveness of ML methodologies in forecasting ED utilization within different age cohorts, identify common reasons for visits within specific age cohorts, and assess the impact of age on the severity and nature of ED presentations. Age serves as a critical predictor in forecasting emergency department (ED) visits. For example, pediatric populations often present with acute illnesses and injuries, while older adults may have higher rates of chronic diseases and complex medical needs. By incorporating age as a variable, SVM models can stratify patients into different risk categories based on age-specific healthcare utilization trends. This allows for more accurate predictions of ED visit frequencies and resource demands tailored to specific age demographics. Furthermore, age-related temporal variations and seasonal effects, such as flu outbreaks or holiday-related injuries, can be integrated into SVM modeling to adjust predictions dynamically. Using The Nationwide Emergency Department Sample (NEDS) ED Visits data from 2006 through 2020, it is a comprehensive collection of data detailing visits to emergency departments (EDs) across the national scale. This dataset is specifically tailored for studying ED visits with a focus on age-related patterns and trends. The dataset reveals that the highest volume of ED visits over the 14 years occurred within the age group of 18–44, averaging approximately 158,056 visits per year. The machine learning (ML) model is refined through iterative optimization. Input data is processed by the ML algorithm, which initially computes model coefficients using an interior point optimization method. In subsequent iterations, the starting point is adapted based on the previous iteration, and the goal is to minimize the objective function, reflecting the difference between predicted and actual patient numbers. The model's impressive 96.7% accuracy shows strong predictive capability, while its high R-squared value of 0.996 indicates an excellent fit to the data, highlighting its reliability and effectiveness in capturing underlying patterns. The predictive model contributes to the efficiency of resource utilization and healthcare service quality by facilitating the identification and early intervention of high-risk patients, improved patient outcomes, and care management. This approach aligns with the broader goals of precision medicine and population health management, ultimately benefiting both patients and healthcare providers.