ISSN 2394-5125
 

Research Article 


Forecasting Hospital Admissions in Emergency Department using Data Mining

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti.

Abstract
The recent study carried out by Institute of Medicine stated that crowding in emergency department (ED) stands as a barrier to provide safe medical
services at the right time. Overcrowding in the emergency departments may have serious negative impacts for patients. So there is a need for the
emergency departments to consider the usage of innovative techniques for improving the patient flow and to prevent the overcrowding. One possible
methodology is the usage of machine learning techniques for the prediction of admissions in ED. This paper uses the regulatory information of (120600
records) customarily collected from two significant hospitals in Ireland for comparing various machine learning algorithms for the prediction of
admission risk in the ED. Generally, 3 algorithms are mostly used to develop the predictive models: (1) Logistic regression, (2) Decision trees and (3)
Gradient Boosted Machines (GBM). Drawing on logistic regression, we have a tendency to determine many factors associated with hospital admissions,
as well as hospital website, age, admission mode, sorting class, past admission within last month, and former admissions within past year. In this paper ,
we highlight the utilization of three algorithms of machine learning for predicting the victim admissions. Implementing the models will offer a snap of
foretold admissions in the ED in a certain time, letting advancement in resource designing and also reducing the crowding. Once interpretability may be a
principal thought, EDs thought to adopt logistic regression model, though GBM is helpful wherever accuracy is predominant.

Key words: Emergency Department, Gradient Boosted Machines, Mean Absolute Percentage Error, Multi-Layer Perceptron, Fuzzy Min-Max.


 
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How to Cite this Article
Pubmed Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti. Forecasting Hospital Admissions in Emergency Department using Data Mining. JCR. 2020; 7(15): 356-362. doi:10.31838/jcr.07.15.49


Web Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti. Forecasting Hospital Admissions in Emergency Department using Data Mining. http://www.jcreview.com/?mno=119763 [Access: June 29, 2020]. doi:10.31838/jcr.07.15.49


AMA (American Medical Association) Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti. Forecasting Hospital Admissions in Emergency Department using Data Mining. JCR. 2020; 7(15): 356-362. doi:10.31838/jcr.07.15.49



Vancouver/ICMJE Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti. Forecasting Hospital Admissions in Emergency Department using Data Mining. JCR. (2020), [cited June 29, 2020]; 7(15): 356-362. doi:10.31838/jcr.07.15.49



Harvard Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti (2020) Forecasting Hospital Admissions in Emergency Department using Data Mining. JCR, 7 (15), 356-362. doi:10.31838/jcr.07.15.49



Turabian Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti. 2020. Forecasting Hospital Admissions in Emergency Department using Data Mining. Journal of Critical Reviews, 7 (15), 356-362. doi:10.31838/jcr.07.15.49



Chicago Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti. "Forecasting Hospital Admissions in Emergency Department using Data Mining." Journal of Critical Reviews 7 (2020), 356-362. doi:10.31838/jcr.07.15.49



MLA (The Modern Language Association) Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti. "Forecasting Hospital Admissions in Emergency Department using Data Mining." Journal of Critical Reviews 7.15 (2020), 356-362. Print. doi:10.31838/jcr.07.15.49



APA (American Psychological Association) Style

Albert Mayan J, Velmurugan A, Nitin Narayanan Kokkoori, Lokesh Koleti (2020) Forecasting Hospital Admissions in Emergency Department using Data Mining. Journal of Critical Reviews, 7 (15), 356-362. doi:10.31838/jcr.07.15.49





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