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

• Published in: Brain sciences 2023-03, Vol.13 (3), p.513
• Main Authors: 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
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Classification; Coronaviruses; Correlation analysis; COVID-19; COVID-19 stress; Data analysis; Data mining; Decision making; decision tree; Decision trees; Disease; Disease transmission; Epidemics; explainable artificial intelligence for healthcare; Health care; healthcare professionals in Mexico; Job stress; Learning algorithms; Machine learning; Medical personnel; Mental disorders; Mental health; Mental illness; Mexico; Pandemics; Professionals; Public health; Questionnaires; Statistical analysis; Statistics; Stress
• ISSN: 2076-3425; 2076-3425
• DOI: 10.3390/brainsci13030513

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 ( < 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.