Abstract
COVID-19 has been spread to many countries all over the world in a relatively short period, largely overwhelmed hospitals have been a direct consequence of the explosive increase of coronavirus cases. In this dire situation, the demand for the development of clinical decision support systems based on predictive algorithms has increased, since these predictive technologies may help to alleviate unmanageable stress on healthcare systems. We contribute to this effort by a comprehensive study over a real dataset of covid-19 patients from a local hospital. The collected dataset is representative of the local policies on data gathering implemented during the pandemic, showing high imabalance and large number of missing values. In this paper, we report a descriptive analysis of the data that points out the large disparity of data in terms of severity and age. Furthermore, we report the results of the principal component analysis (PCA) and Logistic Regression (LR) techniques to find out which variables are the most relevant and their respective weight. The results show that there are two very relevant variables for the detection of the most severe cases, yielding promissing results. One of our paper conclussions is a strong recommendation to the local authorities to improve the data gathering protocols.
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Acknowledgments
The authors would like to express their gratitude to Fundación Vital for the financial support to the project “Aportaciones de Modelos Predictivos para COVID-19 basados en Inteligencia Artificial específicos para el Territorio Histórico de Alava - COVID19THA”. In addition authors thank to the group “Nuevos desarrollos en salud” of Bioaraba and to Osakidetza-Servicio Vasco de Salud for their collaboration.
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Badiola-Zabala, G., Lopez-Guede, J.M., Estevez, J., Graña, M. (2022). On the Analysis of a Real Dataset of COVID-19 Patients in Alava. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2022. Lecture Notes in Computer Science(), vol 13469. Springer, Cham. https://doi.org/10.1007/978-3-031-15471-3_5
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DOI: https://doi.org/10.1007/978-3-031-15471-3_5
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