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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/7839| Clinical applications of machine learning on covid-19: the use of a decision tree algorithm for the assessment of perceived stress in mexican healthcare professionals | |
| ERIKA ZUÑIGA VIOLANTE HÉCTOR FRANCO VILLARREAL Gener Avilés-Rodríguez Gerardo Raymundo Padilla Rivas Cosío_León María de los Ángeles Jose Francisco Islas Gerardo Romo-Cardenas Juan Luis Delgado Gallegos | |
| Acceso Abierto | |
| Atribución-NoComercial | |
| https://doi.org/10.1101/2020.11.18.20233288 | |
| Stress and anxiety have shown to be indirect effects of the COVID-19 pandemic, therefore managing stress becomes essential. One of the most affected populations by the pandemic are healthcare professionals. Thus, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. In our study, we used a machine learning prediction model to help measure perceived stress; a C5.0 decision tree algorithm was used to analyze and classify datasets obtained from healthcare professionals of the northeast region of Mexico. Our analysis showed that 6 out of 102 instances were incorrectly classified. Missing two cases for mild, three for moderate and 1 for severe (accuracy of 94.1%), statistical correlation analysis was performed to ensure integrity of the method, in addition we concluded that severe stress cases can be related mostly to high levels of Xenophobia and Compulsive stress. | |
| Medrxiv | |
| 20-11-2020 | |
| Preimpreso | |
| medrxiv.org/ | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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