Abstract
Proper prediction of Length Of Stay (LOS) has become increasingly important these years. The LOS prediction provides better services, managing hospital resources and controls their costs. In this paper, we implemented and compared two Machine Learning (ML) methods, the Random Forest (RF) and the Gradient Boosting model (GB), using an open source available dataset. This data are been firstly preprocessed by combining data transformation, data standardization and data codification. Then, the RF and the GB were carried out, with a phase of hyper parameters tuning until setting optimal coefficients. Finally, the Mean Square Error (MAE), the R-squared (\(R^{2}\)) and the Adjusted R-squared (Adjusted \(R^{2}\)) metrics are selected to evaluate model with parameters.
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Acknowledgments
The authors would like to thank the region of Hauts-de-France (FEDER funds) and the company Alicante (https://www.alicante.fr/) for the funding and their support and contributions.
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Mekhaldi, R.N., Caulier, P., Chaabane, S., Chraibi, A., Piechowiak, S. (2020). Using Machine Learning Models to Predict the Length of Stay in a Hospital Setting. In: Rocha, Á., Adeli, H., Reis, L., Costanzo, S., Orovic, I., Moreira, F. (eds) Trends and Innovations in Information Systems and Technologies. WorldCIST 2020. Advances in Intelligent Systems and Computing, vol 1159. Springer, Cham. https://doi.org/10.1007/978-3-030-45688-7_21
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DOI: https://doi.org/10.1007/978-3-030-45688-7_21
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