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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7449| Hidden Effects of COVID-19 on Healthcare Workers: A Machine Learning Analysis | |
| Mostafa Rezapour | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| arXiv:2112.06261 | |
| https://arxiv.org/abs/2112.06261 | |
| In this paper, we analyze some effects of the COVID-19 pandemic on healthcare workers. We specifically focus on alcohol consumption habit changes among healthcare workers using a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research. We use supervised and unsupervised machine learning methods and models such as Decision Trees, Logistic Regression, Naive Bayes classifier, k-Nearest Neighbors, Support Vector Machines, Multilayer perceptron, Random Forests, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, Chi-Squared Test and mutual information method to find out relationships between COVID-19 related negative effects and alcohol use changes in healthcare workers. Our findings suggest that some effects of the COVID-19 pandemic such as school closure, work schedule change and COVID-related news exposure may lead to an increase in alcohol use. | |
| Cornell University | |
| 12-12-2021 | |
| Preimpreso | |
| arxiv.org | |
| Inglés | |
| Epidemia COVID-19 | |
| Investigadores Público en general | |
| OTRAS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Artículos científicos |
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| Hidden Effects of COVID 19 on Healthcare Workers_ A Machine Learning Analysis.pdf | 601.36 kB | Adobe PDF | Visualizar/Abrir |