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Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19

Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19...

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Autores principales: Delgado-Gallegos, Juan Luis, Avilés-Rodriguez, Gener, Padilla-Rivas, Gerardo R., De los Ángeles Cosío-León, María, Franco-Villareal, Héctor, Nieto-Hipólito, Juan Iván, de Dios Sánchez López, Juan, Zuñiga-Violante, Erika, Islas, Jose Francisco, Romo-Cardenas, Gerardo Salvador
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046351/
https://www.ncbi.nlm.nih.gov/pubmed/36979323
http://dx.doi.org/10.3390/brainsci13030513
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author Delgado-Gallegos, Juan Luis
Avilés-Rodriguez, Gener
Padilla-Rivas, Gerardo R.
De los Ángeles Cosío-León, María
Franco-Villareal, Héctor
Nieto-Hipólito, Juan Iván
de Dios Sánchez López, Juan
Zuñiga-Violante, Erika
Islas, Jose Francisco
Romo-Cardenas, Gerardo Salvador
author_facet Delgado-Gallegos, Juan Luis
Avilés-Rodriguez, Gener
Padilla-Rivas, Gerardo R.
De los Ángeles Cosío-León, María
Franco-Villareal, Héctor
Nieto-Hipólito, Juan Iván
de Dios Sánchez López, Juan
Zuñiga-Violante, Erika
Islas, Jose Francisco
Romo-Cardenas, Gerardo Salvador
author_sort Delgado-Gallegos, Juan Luis
collection PubMed
description Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation (p < 0.05) and a calculated Cronbach’s alpha of 0.94 and McDonald’s omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of 94.1%). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field.
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spelling pubmed-100463512023-03-29 Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19 Delgado-Gallegos, Juan Luis Avilés-Rodriguez, Gener Padilla-Rivas, Gerardo R. De los Ángeles Cosío-León, María Franco-Villareal, Héctor Nieto-Hipólito, Juan Iván de Dios Sánchez López, Juan Zuñiga-Violante, Erika Islas, Jose Francisco Romo-Cardenas, Gerardo Salvador Brain Sci Article Coronavirus disease (COVID-19) represents one of the greatest challenges to public health in modern history. As the disease continues to spread globally, medical and allied healthcare professionals have become one of the most affected sectors. Stress and anxiety are indirect effects of the COVID-19 pandemic. Therefore, it is paramount to understand and categorize their perceived levels of stress, as it can be a detonating factor leading to mental illness. Here, we propose a computer-based method to better understand stress in healthcare workers facing COVID-19 at the beginning of the pandemic. We based our study on a representative sample of healthcare professionals attending to COVID-19 patients in the northeast region of Mexico, at the beginning of the pandemic. We used a machine learning classification algorithm to obtain a visualization model to analyze perceived stress. The C5.0 decision tree algorithm was used to study datasets. We carried out an initial preprocessing statistical analysis for a group of 101 participants. We performed chi-square tests for all questions, individually, in order to validate stress level calculation (p < 0.05) and a calculated Cronbach’s alpha of 0.94 and McDonald’s omega of 0.95, demonstrating good internal consistency in the dataset. The obtained model failed to classify only 6 out of the 101, missing two cases for mild, three for moderate and one for severe (accuracy of 94.1%). We performed statistical correlation analysis to ensure integrity of the method. In addition, based on the decision tree model, we concluded that severe stress cases can be related mostly to high levels of xenophobia and compulsive stress. Thus, showing that applied machine learning algorithms represent valuable tools in the assessment of perceived stress, which can potentially be adapted to other areas of the medical field. MDPI 2023-03-20 /pmc/articles/PMC10046351/ /pubmed/36979323 http://dx.doi.org/10.3390/brainsci13030513 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Delgado-Gallegos, Juan Luis
Avilés-Rodriguez, Gener
Padilla-Rivas, Gerardo R.
De los Ángeles Cosío-León, María
Franco-Villareal, Héctor
Nieto-Hipólito, Juan Iván
de Dios Sánchez López, Juan
Zuñiga-Violante, Erika
Islas, Jose Francisco
Romo-Cardenas, Gerardo Salvador
Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19
title Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19
title_full Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19
title_fullStr Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19
title_full_unstemmed Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19
title_short Application of C5.0 Algorithm for the Assessment of Perceived Stress in Healthcare Professionals Attending COVID-19
title_sort application of c5.0 algorithm for the assessment of perceived stress in healthcare professionals attending covid-19
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046351/
https://www.ncbi.nlm.nih.gov/pubmed/36979323
http://dx.doi.org/10.3390/brainsci13030513
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