A Machine Learning Analysis of Impact of the Covid-19 Pandemic on Alcohol Consumption Habit Changes Among Healthcare Workers in the U.S
This addresses mental health impacts on healthcare workers during the pandemic, but is incremental as it applies standard ML methods to new survey data.
This study analyzed how the COVID-19 pandemic affected alcohol consumption habits among U.S. healthcare workers using various machine learning methods on survey data, finding that COVID-19-related school closures, work schedule changes, and news exposure were associated with increased alcohol use.
In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States. We utilize multiple supervised and unsupervised machine learning methods and models such as Decision Trees, Logistic Regression, Naive Bayes classifier, k-Nearest Neighbors, Support Vector Machines, Multilayer perceptron, XGBoost, CatBoost, LightGBM, Chi-Squared Test and mutual information method on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to find out relationships between COVID-19 related negative effects and alcohol consumption habit changes among healthcare workers. Our findings suggest that COVID-19-related school closures, COVID-19-related work schedule changes and COVID-related news exposure may lead to an increase in alcohol use among healthcare workers in the United States.