An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff

PLoS One. 2023 Jun 8;18(6):e0286155. doi: 10.1371/journal.pone.0286155. eCollection 2023.

Abstract

The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers during and after the outbreak. The study's objective is to evaluate COVID-19's psychological effects on healthcare professionals of Saudi Arabia. Healthcare professionals from tertiary teaching hospitals were invited to participate in the survey. Almost 610 people participated in the survey, of whom 74.3% were female, and 25.7% were male. The survey included the ratio of Saudi and non-Saudi participants. The study has utilized multiple machine learning algorithms and techniques such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The machine learning models offer 99% accuracy for the credentials added to the dataset. The dataset covers several aspects of medical workers, such as profession, working area, years of experience, nationalities, and sleeping patterns. The study concluded that most of the participants who belonged to the medical department faced varying degrees of anxiety and depression. The results reveal considerable rates of anxiety and depression in Saudi frontline workers.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Anxiety / epidemiology
  • Anxiety / psychology
  • COVID-19* / epidemiology
  • COVID-19* / psychology
  • Health Personnel / psychology
  • Humans
  • Medical Staff
  • Mental Health
  • SARS-CoV-2

Grants and funding

The authors acknowledge the support from the Deanship of Scientific Research, Najran University, Kingdom of Saudi Arabia, for funding this work under the Research Groups funding program grant code number (NU/RG/SERC/11/7). It is a team work and international researchers have cooperated with us in problem identification, formulation, visualization, data analysis, and conceptualization. Also, the funder will support 500 USD fee payment if the paper is accepted for publication.