Abstract:
The hospital Emergency Department (ED) has become the main point of entry for patients
in modern hospitals, resulting in frequent overcrowding; as a result, hospital management
is increasingly paying attention to the ED to provide better quality service to patients.
This study seeks to build time series (Autoregressive Integrated Moving Average) and
machine learning (XGBoost, Gradient Boosting Regressor and Voting Regressor) regressor
models, evaluate the performance of each and use the best model to forecast daily
attendance. A comprehensive analysis of data related to patient arrivals at a hospital, focusing on different times of day is performed. The study was conducted in the Emergency
Department of a specified South African public hospital. A dataset of patient arrivals
from May 2019 to November 2021 has been collected, with a total of 47 461 observations
used for the analysis. A time series model and three machine learning regressor models
were investigated.
Detailed statistical and exploratory analyses, time series plots, model training, and
model validation efforts are carried out. The study delves into various aspects such as
stationarity testing, normality testing, and the use of different transformation methods to
achieve stationarity. Machine Learning algorithms are employed, with a hyperparameter
tuning phase to obtain optimal coefficients. The evaluation matrices Mean Absolute
Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE),
Root Mean Squared Error (RMSE) and Mean Percentage Difference (MPD). Lastly, the
chosen model is used to forecast Normal Hours and After Hours.
The Voting Regressor emerged as the most reliable, showing consistent performance
across both training and test datasets, whereas models like ARIMA and XGBoost struggled
with autocorrelation issues and peak predictions, respectively. Overall, while the
Gradient Boosting Regressor performed well on training data, it exhibited potential overfitting, suggesting the Voting Regressor as the preferable model for handling the complex patterns of patient arrivals.
