Machine learning-based analytics of the impact of the Covid-19 pandemic on alcohol consumption habit changes among United States healthcare workers

Abstract

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States.

Figure 1

The schematic diagram of splitting the data into train, validation, and test sets.

Figure 2

The AUROCs of XGBoost with 4 trees (maximum depth equals 3) on training, validation, and test sets as well as SHAP values of XGBoost with 4 trees of maximum depth of 3 (XGB-4) before and after SMOTE is applied.

Figure 3

The AUROCs of AdaBoost classifier with 8 stumps on training, validation, and test sets as well as the top predictors of the model.

Figure 4

Top predictors of AdaBoost with 8 stumps in absence of Question 20 before and after SMOTE is applied to the training sets.

Figure 5

The schematic diagram of clustering method and determining the most important features within each cluster.

Figure 6

Training, validation, and test AUROCs of LightGBM with 6 trees of maximum depth equals 3 (LGBM-6) as well as the top predictors within each group.

Figure 6

Training, validation, and test AUROCs of LightGBM with 6 trees of maximum depth equals 3 (LGBM-6) as well as the top predictors within each group.