Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid

Abstract

Anticipating future workloads in a hospital may be of capital importance in order to distribute resources and improve patient attention. In this paper, we tackle the problem of predicting daily hospital admissions in Madrid due to circulatory and respiratory cases based on biometeorological indicators. A range of forecasting algorithms were proposed covering four model families: ensemble methods, boosting methods, artificial neural networks and ARIMA. Experiments show how the last two obtain better results in average, demonstrating that the problem can be properly solved with both approaches. Furthermore, a recently proposed technique known as stacked generalization was also used to dynamically combine the predictions from the four models, finally improving the performance with respect to the individual models.

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