Cargando…
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...
Autores principales: | , , , , , , , , , |
---|---|
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 |
_version_ | 1785013651066847232 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10046351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT delgadogallegosjuanluis applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT avilesrodriguezgener applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT padillarivasgerardor applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT delosangelescosioleonmaria applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT francovillarealhector applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT nietohipolitojuanivan applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT dediossanchezlopezjuan applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT zunigaviolanteerika applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT islasjosefrancisco applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 AT romocardenasgerardosalvador applicationofc50algorithmfortheassessmentofperceivedstressinhealthcareprofessionalsattendingcovid19 |