# A Machine Learning Analysis of COVID-19 Mental Health Data

arXiv: https://arxiv.org/abs/2112.00227 · HTML: https://mlsanity.com/paper/2112.00227/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [A New Multipoint Symmetric Secant Method with a Dense Initial Matrix](https://mlsanity.com/paper/2107.06321/) (2021)

## Built on by (most cited first, 6 of 6)

- [NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media](https://mlsanity.com/paper/2301.11004/) (2023)
- [A machine learning analysis of the relationship between some underlying medical conditions and COVID-19 susceptibility](https://mlsanity.com/paper/2112.12901/) (2021)
- [Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Health](https://mlsanity.com/paper/2202.07441/) (2022)
- [GAME: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorders](https://mlsanity.com/paper/2309.10077/) (2023)
- [A Machine Learning Analysis of Impact of the Covid-19 Pandemic on Alcohol Consumption Habit Changes Among Healthcare Workers in the U.S](https://mlsanity.com/paper/2112.06261/) (2021)
- [Machine Learning Based Analytics for the Significance of Gait Analysis in Monitoring and Managing Lower Extremity Injuries](https://mlsanity.com/paper/2309.15990/) (2023)

## Abstract (arXiv)

In late December 2019, the novel coronavirus (Sars-Cov-2) and the resulting disease COVID-19 were first identified in Wuhan China. The disease slipped through containment measures, with the first known case in the United States being identified on January 20th, 2020. In this paper, we utilize survey data from the Inter-university Consortium for Political and Social Research and apply several statistical and machine learning models and techniques such as Decision Trees, Multinomial Logistic Regression, Naive Bayes, k-Nearest Neighbors, Support Vector Machines, Neural Networks, Random Forests, Gradient Tree Boosting, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, and Chi-Squared Test to analyze the impacts the COVID-19 pandemic has had on the mental health of frontline workers in the United States. Through the interpretation of the many models applied to the mental health survey data, we have concluded that the most important factor in predicting the mental health decline of a frontline worker is the healthcare role the individual is in (Nurse, Emergency Room Staff, Surgeon, etc.), followed by the amount of sleep the individual has had in the last week, the amount of COVID-19 related news an individual has consumed on average in a day, the age of the worker, and the usage of alcohol and cannabis.