Modeling of COVID-19 Clinical Outcomes in Mexico: An Analysis of Demographic, Clinical, and Chronic Disease Factors

Author

Livia Clarete, The Graduate Center, City University of New YorkFollow

Date of Degree

2-2024

Document Type

Capstone Project

Degree Name

M.S.

Program

Data Analysis & Visualization

Advisor

Howard Thomas Everson

Subject Categories

Data Science | Medical Humanities | Statistical Models

Keywords

covid-19

Abstract

This study explores COVID-19 clinical outcomes in Mexico, focusing on demographic, clinical, and chronic disease variables to develop predictive models. In the binary classification task, the Ada Boost Classifier distinguishes survivors from non-survivors, with age, sex, ethnicity, and chronic medical conditions influencing outcomes. In multiclass classification, the Gradient Boosting Classifier categorizes patients into outcome groups.

Demographic variables, especially age, are crucial for predicting COVID-19 outcomes for both the binary and multiclass classification tasks. Clinical information about previous conditions, including chronic diseases, also holds relevance, especially diabetes, immunocompromise, and cardiovascular diseases. These insights inform public health measures and healthcare strategies, emphasizing demographic variables in addressing COVID-19 challenges in Mexico.

Recommended Citation

Clarete, Livia, "Modeling of COVID-19 Clinical Outcomes in Mexico: An Analysis of Demographic, Clinical, and Chronic Disease Factors" (2024). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/5702