Predictive Modeling of Stress in the Healthcare Industry During COVID-19: A Novel Approach Using XGBoost, SHAP Values, and Tree Explainer

Pooja Gupta, Srabanti Maji and Ritika Mehra
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Pooja Gupta: DIT University, India
Srabanti Maji: DIT University, India
Ritika Mehra: Dev Bhoomi Uttarakhand University, India

International Journal of Decision Support System Technology (IJDSST), 2023, vol. 15, issue 1, 1-20

Abstract: There was a substantial medicine shortage and an increase in morbidity due to the second wave of the COVID-19 pandemic in India. This pandemic has also had a drastic impact on healthcare professionals' psychological health as they were surrounded by suffering, death, and isolation. Healthcare practitioners in North India were sent a self-administered questionnaire based on the COVID-19 Stress Scale (N = 436) from March to May 2021. With 10-fold cross-validation, extreme gradient boosting (XGBoost) was used to predict the individual stress levels. XGBoost classifier was applied, and classification accuracy was 88%. The results of this research show that approximately 52.6% of healthcare specialists in the dataset exceed the severe psychiatric morbidity standards. Further, to determine which attribute had a significant impact on stress prediction, advanced techniques (SHAP values), and tree explainer were applied. The two most significant stress predictors were found to be medicine shortage and trouble in concentrating.

Date: 2023
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