Predictive Modeling of Healthcare Professional's Stress Based on XGBoost Model
18th IEEE India Council International Conference, INDICON 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1752412
ABSTRACT
In India, the second wave of the COVID-19 pandemic has resulted in a significant shortage of medicines and increased morbidity. COVID-19 has also had a profound influence on the psychological well-being of health professionals, who are surrounded by agony, death, and isolation as a result of the epidemic. The goal of this cross-sectional study is to look into the mental health of Indian healthcare workers during the second wave of the COVID-19 outbreak. From March 2021 to May 2021, a self-administered questionnaire based on the COVID-19 Stress Scale was delivered online to healthcare professionals (N = 836) in north India. An ensemble learning technique - Extreme Gradient Boosting (XGBoost) was applied to predict individual stress levels with 10-fold cross-validation. XGBoost had predicted stress with an average accuracy of 0.8889. According to the findings of this study, around 52.6 percent of healthcare professionals in the sample meet the threshold for severe psychiatric morbidity. In addition, advanced methodologies (SHAP values) were employed to determine which features had a significant impact on stress prediction. Medicine shortages and trouble concentrating were found to be the two most significant CSS predictors. © 2021 IEEE.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
18th IEEE India Council International Conference, INDICON 2021
Year:
2021
Document Type:
Article
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