A Comparative Machine Learning Approaches for Patient Flow Forecasting in an Emergency Department during the COVID-19

Abstract

The Corona Virus Disease 2019 (COVID-19) has impacted numerous areas of the health system. In fact, it made the world work remotely during several months and created an assorted uncertainty for medical service recipients. Thus, anticipating novel everyday patient income in relation to the COVID-19 has become pivotal for clinical, political, and different authorities who handle on a daily basis, COVID-19 related planned operations. Current machine learning draws near, in an attempt to get dynamic results. This work intends to demonstrate the wayan Emergency Department (ED) is able to use machine-learning approaches during the daily patient flow forecasting for better management in an emergency department. Thus, it is essential to test five different supervised machine-learning approaches by evaluating their coefficient of determination (R2) to figure the everyday patient flow income for better management.