Data Mining for Forecasting ED Patients' Admittance to Hospitals

Abstract

There is a risk that people may suffer serious harm as a result of overcrowding in emergency departments (EDs). As a result, emergency
clinics must explore employing innovative techniques to boost patient flow while simultaneously minimising congestion in the waiting area.
One option for projecting emergency department admissions is to use data mining and machine learning technologies to anticipate ED
admissions. This study, which takes use of routinely obtained administrative data (120 600 records) from two major acute hospitals in
Northern Ireland, presents a comparison of two rival machine learning algorithms for predicting the risk of admission from the emergency
department at the hospital. Three algorithms are used in the process of developing the prediction models: A decision tree may be divided into
three types, which are as follows: Decision trees include: 1) decision trees, 2) gradient boosted machines, and 3) logistic regression, which are
all types of decision trees (GBM). The GBM has an AUC-ROC D of 0:824 which was better than both the decision tree and the logistic
regression model (accuracy D 80:06 percent, AUC-ROC D 0:824). In this case, the accuracy is 80:06 percent and the AUC-ROC is 0:824. In
this situation, the accuracy is 80:31 percent, and the AUC-ROC is 0:859, which indicates a good fit. (0:849) (0:849) (AUC-ROC D 0:849)
(accuracy D 79:94 percent). We discovered a number of factors that were connected with hospital admissions via the application of logistic
regression. These considerations included hospital location, age, arrival mode, triage category, care group, and previous hospitalisation
during the previous month or year, among other things. This study highlights the potential value of machine learning systems by using three
fundamental machine learning algorithms to predict patient admissions. Decision support systems may be able to offer a picture of expected
ED admissions at any given moment as a consequence of this study, allowing for resource planning ahead of time and avoiding patient flow
bottlenecks. This research also suggests that the models described in this study may be utilised to perform comparisons between projected and
actual admission rates. Generalised bivariate models (GBMs) are sufficient when interpretability is a concern; however, if accuracy is crucial,
logistic regression models should be considered.