# A machine learning analysis of the relationship between some underlying medical conditions and COVID-19 susceptibility

arXiv: https://arxiv.org/abs/2112.12901 · HTML: https://mlsanity.com/paper/2112.12901/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 24 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [On Solving L-SR1 Trust-Region Subproblems](https://mlsanity.com/paper/1506.07222/) (2015)
- [A Machine Learning Analysis of COVID-19 Mental Health Data](https://mlsanity.com/paper/2112.00227/) (2021)
- [A New Multipoint Symmetric Secant Method with a Dense Initial Matrix](https://mlsanity.com/paper/2107.06321/) (2021)

## Built on by (most cited first, 1 of 1)

- [Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Health](https://mlsanity.com/paper/2202.07441/) (2022)

## Abstract (arXiv)

For the past couple years, the Coronavirus, commonly known as COVID-19, has significantly affected the daily lives of all citizens residing in the United States by imposing several, fatal health risks that cannot go unnoticed. In response to the growing fear and danger COVID-19 inflicts upon societies in the USA, several vaccines and boosters have been created as a permanent remedy for individuals to take advantage of. In this paper, we investigate the relationship between the COVID-19 vaccines and boosters and the total case count for the Coronavirus across multiple states in the USA. Additionally, this paper discusses the relationship between several, selected underlying health conditions with COVID-19. To discuss these relationships effectively, this paper will utilize statistical tests and machine learning methods for analysis and discussion purposes. Furthermore, this paper reflects upon conclusions made about the relationship between educational attainment, race, and COVID-19 and the possible connections that can be established with underlying health conditions, vaccination rates, and COVID-19 total case and death counts.