Full Citation

Title: Analysis of Factors Influencing the Severity of Coronavirus Symptoms Using Predictive Modeling

Citation Type: Conference Paper

Publication Year: 2023

ISBN: 9798350327595

ISSN:

DOI: 10.1109/CSCE60160.2023.00030

NSFID:

PMCID:

PMID:

Abstract: This paper presents a case study on the IPUMS NHIS database, which provides data from censuses and surveys on the health of the U.S. population, including data related to COVID-19. By addressing gaps in previous studies, we propose a machine learning approach to train predictive models for identifying and measuring factors that affect the severity of COVID-19 symptoms. Our experiments focus on four groups of factors: demographic, socio-economic, health condition, and related to COVID-19 vaccination. By analysing the sensitivity of the variables used to train the models and the variable effect characteristics (VEC) analysis on the variable values, we identify and measure importance of various factors that influence the severity of COVID-19 symptoms.

Url: https://ieeexplore.ieee.org/abstract/document/10487448

User Submitted?: No

Authors: Nachev, Anatoli

Conference Name: 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

Publisher Location:

Data Collections: IPUMS Health Surveys - NHIS

Topics: Health

Countries: