TY - GEN
T1 - Analysis of Factors Influencing the Severity of Coronavirus Symptoms Using Predictive Modeling
AU - Nachev, Anatoli
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - classification
KW - COVID-19
KW - logistic regression
KW - models
KW - supervised learning
UR - https://www.scopus.com/pages/publications/85191167274
U2 - 10.1109/CSCE60160.2023.00030
DO - 10.1109/CSCE60160.2023.00030
M3 - Conference Publication
AN - SCOPUS:85191167274
T3 - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
SP - 157
EP - 162
BT - Proceedings - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023
Y2 - 24 July 2023 through 27 July 2023
ER -