MACHINE LEARNING TECHNIQUES TO PREDICT THE HOSPITAL ADMISSIONS FROM EMERGENCY DEPARTMENTS

Healthcare companies frequently profit from information technologies as well as enclosed decision support methods, which enhance the quality of services (QoS) and support to limit difficulties and conflicting effects. To be prepared to predict, at the time of triage, whether a requirement for hospital access endures for emergency department (ED) patients may discover valuable data that could provide to system-wide hospital improvements intended to advance ED throughput. The main objective of this study is to improve and prove a predictive model to evaluate whether a patient is expected to need inpatient access at the time of ED triage, using regular hospital official data. The Machine Learning schemes for healthcare in developing algorithms applied to recognize the complex patterns with big data. In this paper, we use three machine learning algorithms to improve the predictive models: (1) logistic regression (LR), (2) Modified Random Forest (MRF), and (3) gradient boosted machines (GBM). The GBM performed better (accuracy=82.41%) than the modified random forest (accuracy=81.09%) and the logistic regression model (accuracy=80.54%). Drawing on logistic regression, we recognize various hospital admissions circumstances, including hospital site, care group, age, arrival mode, attributes category, the last admission in the past month, and earlier access in the past year. This research highlights the possible benefit of three standard machine learning algorithms in predicting patient admissions.