Full Citation

Title: Predictive Modeling for Analysis of Coronavirus Symptoms Using Logistic Regression

Citation Type: Journal Article

Publication Year: 2023

ISBN:

ISSN:

DOI: 10.17265/2159-5275/2023.04.001

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, socioeconomic , health condition, and related to COVID-19 vaccination. By analysing the sensitivity of the variables used to train the models and the VEC (variable effect characteristics) analysis on the variable values, we identify and measure importance of various factors that influence the severity of COVID-19 symptoms.

Url: https://www.davidpublisher.com/Public/uploads/Contribute/658a71f5e88e1.pdf

User Submitted?: No

Authors: Nachev, Anatoli

Periodical (Full): Journal of Mechanics Engineering and Automation

Issue:

Volume: 13

Pages: 93-99

Data Collections: IPUMS Health Surveys - NHIS

Topics: Health

Countries: