Authors:
Alexander Zlotnik
1
;
Juan Manuel Montero Martínez
2
and
Ascención Gallardo-Antolín
3
Affiliations:
1
Politecnic University of Madrid, ETSI Telecomunicación and Ramón y Cajal University Hospital, Spain
;
2
Politecnic University of Madrid and ETSI Telecomunicación, Spain
;
3
Carlos III University, Spain
Keyword(s):
Forecasting, Emergency Service, Emergency Department, Hospital, Operations Research, SVM, Time Series Analysis, ARIMA, SARIMA.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Decision Support Systems
;
Enterprise Information Systems
;
Health Information Systems
;
Healthcare Management Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
A comparison of multivariate SARIMA model with a multivariate regression-based time series based on a Support Vector Machine model was performed for emergency department admissions prediction. The same input variables were used in both models. Both models were trained with consecutive daily samples of data corresponding to the January 2009 – August 2012 period (n=1339). Performance was evaluated on the September 2012 test dataset (n=30). The results obtained with the Support Vector Machine were found to be more accurate with a 46,53% RMSE improvement and a 48,89% MAE improvement on the train set. The experiment was repeated six times with varying time periods. The SVM approach produced better results in all cases. Error measurements on the test set were compared with a paired T test. The differences between all comparisons were found to be statistically significant in all cases with a 95% CI.