Skip to Main content Skip to Navigation
Conference papers

Using Machine Learning Models to Predict the Length of Stay in a Hospital Setting

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 compared two Machine Learning (ML) methods on the Microsoft available dataset. This data are been firstly preprocessed by combining data transformation, data standardization and data codification. Then, the Random Forest (RF) and the Gradient Boosting model (GB) were carried out, with a phase of hyper parameters tuning until setting optimal coefficients. Finally, the Mean Square Error (MAE), R-squared (R 2) and the Adjusted R-squared (Adjusted R 2) metrics are selected to evaluate model with parameters. The best model is saved to be trained with real data later using transfer learning techniques.
Document type :
Conference papers
Complete list of metadata

https://hal-uphf.archives-ouvertes.fr/hal-03381824
Contributor : Mylène Delrue Connect in order to contact the contributor
Submitted on : Monday, October 18, 2021 - 8:14:06 AM
Last modification on : Wednesday, November 3, 2021 - 6:29:43 AM

Identifiers

  • HAL Id : hal-03381824, version 1

Collections

Citation

Rachda Naila Mekhaldi, Patrice Caulier, Sondès Chaabane, Abdelahad Chraibi, Sylvain Piechowiak. Using Machine Learning Models to Predict the Length of Stay in a Hospital Setting. 8th World Conference on Information Systems and Technologies (WorldCIST’2020), Apr 2020, Budva, Montenegro. pp.202-211. ⟨hal-03381824⟩

Share

Metrics

Record views

14