Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis

Carlos Pena; Florencio Peralta; Remigio Hurtado

doi:10.1109/ROPEC50909.2020.9258683

Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis

Carlos Pena, Florencio Peralta, Remigio Hurtado

Artificial Intelligence and Assistive Technologies Research Group (GI-IATa)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This research assesses the health conditions of the people in the study and determines the reason why a person dies after being infected with COVID-19. In this study, 538 sample groups that provided medical data from people in different locations were analyzed. The biggest challenge in this study was to carry out 2 different criteria within the same data set to conclude that the mortality of the persons inside a group depends more than anything on the age of the person at risk and the presence of one or more other health disorders of the primary disease, which in this case is COVID-19. For this study, the public data set 'COVID analytics' was used, which provided all the necessary medical information and the classification of the groups, which are then interpreted as useful labels to better deduce the degree of mortality of the affected person. After completing the data analysis, it is determined that the factors that aggravate the condition of a patient with COVID-19 are: hypertension, advanced age and any other disease.

Original language	English
Title of host publication	2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728199535
DOIs	https://doi.org/10.1109/ROPEC50909.2020.9258683
State	Published - 4 Nov 2020
Event	2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020 - Ixtapa, Mexico Duration: 4 Nov 2020 → 6 Nov 2020

Publication series

Name	2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020

Conference

Conference	2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020
Country/Territory	Mexico
City	Ixtapa
Period	4/11/20 → 6/11/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

Clustering
Covid-19
Linear regression
Multi-linear regression
Polynomial regression
Quadratic regression

Access to Document

10.1109/ROPEC50909.2020.9258683

Cite this

Pena, C., Peralta, F., & Hurtado, R. (2020). Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis. In 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020 Article 9258683 (2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ROPEC50909.2020.9258683

Pena, Carlos ; Peralta, Florencio ; Hurtado, Remigio. / Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis. 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. (2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020).

@inproceedings{5d7a4789f88e40ddb45b5b1defe72edf,

title = "Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis",

abstract = "This research assesses the health conditions of the people in the study and determines the reason why a person dies after being infected with COVID-19. In this study, 538 sample groups that provided medical data from people in different locations were analyzed. The biggest challenge in this study was to carry out 2 different criteria within the same data set to conclude that the mortality of the persons inside a group depends more than anything on the age of the person at risk and the presence of one or more other health disorders of the primary disease, which in this case is COVID-19. For this study, the public data set 'COVID analytics' was used, which provided all the necessary medical information and the classification of the groups, which are then interpreted as useful labels to better deduce the degree of mortality of the affected person. After completing the data analysis, it is determined that the factors that aggravate the condition of a patient with COVID-19 are: hypertension, advanced age and any other disease. ",

keywords = "Clustering, Covid-19, Linear regression, Multi-linear regression, Polynomial regression, Quadratic regression",

author = "Carlos Pena and Florencio Peralta and Remigio Hurtado",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020 ; Conference date: 04-11-2020 Through 06-11-2020",

year = "2020",

month = nov,

day = "4",

doi = "10.1109/ROPEC50909.2020.9258683",

language = "English",

series = "2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020",

address = "United States",

}

Pena, C, Peralta, F & Hurtado, R 2020, Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis. in 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020., 9258683, 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020, Ixtapa, Mexico, 4/11/20. https://doi.org/10.1109/ROPEC50909.2020.9258683

Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis. / Pena, Carlos; Peralta, Florencio; Hurtado, Remigio.
2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9258683 (2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis

AU - Pena, Carlos

AU - Peralta, Florencio

AU - Hurtado, Remigio

PY - 2020/11/4

Y1 - 2020/11/4

N2 - This research assesses the health conditions of the people in the study and determines the reason why a person dies after being infected with COVID-19. In this study, 538 sample groups that provided medical data from people in different locations were analyzed. The biggest challenge in this study was to carry out 2 different criteria within the same data set to conclude that the mortality of the persons inside a group depends more than anything on the age of the person at risk and the presence of one or more other health disorders of the primary disease, which in this case is COVID-19. For this study, the public data set 'COVID analytics' was used, which provided all the necessary medical information and the classification of the groups, which are then interpreted as useful labels to better deduce the degree of mortality of the affected person. After completing the data analysis, it is determined that the factors that aggravate the condition of a patient with COVID-19 are: hypertension, advanced age and any other disease.

AB - This research assesses the health conditions of the people in the study and determines the reason why a person dies after being infected with COVID-19. In this study, 538 sample groups that provided medical data from people in different locations were analyzed. The biggest challenge in this study was to carry out 2 different criteria within the same data set to conclude that the mortality of the persons inside a group depends more than anything on the age of the person at risk and the presence of one or more other health disorders of the primary disease, which in this case is COVID-19. For this study, the public data set 'COVID analytics' was used, which provided all the necessary medical information and the classification of the groups, which are then interpreted as useful labels to better deduce the degree of mortality of the affected person. After completing the data analysis, it is determined that the factors that aggravate the condition of a patient with COVID-19 are: hypertension, advanced age and any other disease.

KW - Clustering

KW - Covid-19

KW - Linear regression

KW - Multi-linear regression

KW - Polynomial regression

KW - Quadratic regression

UR - http://www.scopus.com/inward/record.url?scp=85097977075&partnerID=8YFLogxK

U2 - 10.1109/ROPEC50909.2020.9258683

DO - 10.1109/ROPEC50909.2020.9258683

M3 - Conference contribution

AN - SCOPUS:85097977075

T3 - 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020

BT - 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020

Y2 - 4 November 2020 through 6 November 2020

ER -

Pena C, Peralta F, Hurtado R. Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis. In 2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9258683. (2020 IEEE International Autumn Meeting on Power, Electronics and Computing, ROPEC 2020). doi: 10.1109/ROPEC50909.2020.9258683

Subgroup classification model identifying the most influential factors in the mortality of patients with COVID-19 using data analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this