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

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, Ritika Mehra
Copyright: © 2023 |Pages: 20
DOI: 10.4018/IJDSST.315758
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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.
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In recent times, some researchers have worked on the detection of stress during COVID-19. Numerous machine learning methods have been used to predict stress. (Eder et al, 2021) collected data from 533 participants over seven weeks using the Perceived Vulnerability to Disease Scale. They applied two machine learning models: LASSO for the linear model and ERT for the non-linear model. The most important factors that contributed to fear were identified as worrying about food shortage and concerning the outbreak and its ramifications The focus of this study was primarily on fear and perceived health. Another study proposed by (Praveen S.V. et al, 2021) analyzed the feelings of Indian citizens who had worry, stress, and trauma as a result of COVID-19. For this research, they gathered 840000 tweets. COVID-19 lockdown and Death were determined as the most critical variables that induce stress using natural language processing.

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