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On the Intensive Care Unit Admission During the COVID-19 Pandemic in the Region of Lleida, Spain: A Machine Learning Study

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Proceedings of ELM 2021 (ELM 2021)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 16))

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Abstract

The coronavirus disease 2019 (COVID-19) is an infectious and high transmissible disease that may cause severe illness. Some severe cases might require intensive care unit (ICU) admission because, in some cases, patients had comorbidities and several previous symptoms that can worst their condition. Therefore, it is of paramount importance to find strategies for helping manage the occupancy of ICU units. In this paper, we present a predictive model about ICU admission. We trained different machine learning (ML) models using a dataset containing previous symptoms and risk factors, such as demographics. The algorithms used were Random Forest (RF), Logistic Regression (LR), and Extreme Learning Machine (ELM), and the metrics used to evaluate them were accuracy, balanced accuracy, sensitivity, and specificity. We used RandomOverSampling (ROS), NearMiss (NM), and SMOTE algorithms to balance the dataset at different proportions. The best RF obtained model got a specificity of around 97%. The best LR model gives specificity about 93% and, the best ELM obtained a specificity of 94%. These results demonstrate the excellent performance of these algorithms in these kinds of datasets. Moreover, our findings show that ROS and SMOTE performed better than NM.

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Acknowledgments

The authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources.

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Authors and Affiliations

  1. Department of Computer Engineering, University of Lleida, C/ de Jaume II, 25001, Lleida, Spain

    Didac Florensa, Jordi Mateo & Francesc Solsona

  2. Institue of Biomedical Research of Lleida, Av. Alcalde Rovira Roure 80, 25198, Lleida, Spain

    Pere Godoy

  3. Department of Business Management and Analytics, Arcada University of Applied Sciences, Jan-Magnus Janssons plats 1, 00560, Helsinki, Finland

    Leonardo Espinosa-Leal

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  1. Didac Florensa
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Corresponding author

Correspondence to Leonardo Espinosa-Leal .

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  1. Graduate School and Research, Arcada University of Applied Sciences, Helsinki, Finland

    Kaj-Mikael Björk

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Florensa, D., Mateo, J., Solsona, F., Godoy, P., Espinosa-Leal, L. (2023). On the Intensive Care Unit Admission During the COVID-19 Pandemic in the Region of Lleida, Spain: A Machine Learning Study. In: Björk, KM. (eds) Proceedings of ELM 2021. ELM 2021. Proceedings in Adaptation, Learning and Optimization, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-031-21678-7_9

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