Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature

Luis Herrera-Huisa; Nicole Arias-Meza; Michael Cabanillas-Carbonell

doi:10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110

Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature

Luis Herrera-Huisa
, Nicole Arias-Meza
, Michael Cabanillas-Carbonell

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

Abstract

The world is currently experiencing a major pandemic with the SARS-CoV-2 virus in which many patients who suffer and have suffered from this disease are more likely to suffer from hypertension. For this purpose, we have carried out a review of the scientific literature, from which we have collected 105 articles obtained from the following databases: ProQuest, Dialnet, ScienceDirect, Scopus, IEEE Xplore. Subsequently, based on the inclusion and exclusion criteria, 68 articles were systematized, detailing that Machine Learning helps us in the detection and prediction of hypertension in patients with coronavirus, Likewise, the predictive models that allow better detection of hypertension in patients with Covid 19 are 'Neural Networks', 'Cox Risk Model', 'Random Forest' and 'XGBoost', detailing the countries and technologies used.

Original language	English
Title of host publication	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
Pages	769-775
Number of pages	7
ISBN (Electronic)	9781665435741
DOIs	https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110
State	Published - 2021
Externally published	Yes
Event	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 - New York, United States Duration: 30 Sep 2021 → 3 Oct 2021

Publication series

Name	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

Conference

Conference	19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021
Country/Territory	United States
City	New York
Period	30/09/21 → 3/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Hypertension
Machine learning
Systematic review

Access to Document

10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110

Cite this

Herrera-Huisa, L., Arias-Meza, N., & Cabanillas-Carbonell, M. (2021). Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature. In 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021 (pp. 769-775). (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021). https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110

Herrera-Huisa, Luis ; Arias-Meza, Nicole ; Cabanillas-Carbonell, Michael. / Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature. 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. 2021. pp. 769-775 (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021).

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title = "Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature",

abstract = "The world is currently experiencing a major pandemic with the SARS-CoV-2 virus in which many patients who suffer and have suffered from this disease are more likely to suffer from hypertension. For this purpose, we have carried out a review of the scientific literature, from which we have collected 105 articles obtained from the following databases: ProQuest, Dialnet, ScienceDirect, Scopus, IEEE Xplore. Subsequently, based on the inclusion and exclusion criteria, 68 articles were systematized, detailing that Machine Learning helps us in the detection and prediction of hypertension in patients with coronavirus, Likewise, the predictive models that allow better detection of hypertension in patients with Covid 19 are 'Neural Networks', 'Cox Risk Model', 'Random Forest' and 'XGBoost', detailing the countries and technologies used.",

keywords = "Hypertension, Machine learning, Systematic review",

author = "Luis Herrera-Huisa and Nicole Arias-Meza and Michael Cabanillas-Carbonell",

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year = "2021",

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language = "English",

series = "19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021",

pages = "769--775",

booktitle = "19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021",

}

Herrera-Huisa, L, Arias-Meza, N & Cabanillas-Carbonell, M 2021, Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature. in 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, pp. 769-775, 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021, New York, United States, 30/09/21. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110

Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature. / Herrera-Huisa, Luis; Arias-Meza, Nicole; Cabanillas-Carbonell, Michael.
19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. 2021. p. 769-775 (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021).

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

TY - GEN

T1 - Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature

AU - Herrera-Huisa, Luis

AU - Arias-Meza, Nicole

AU - Cabanillas-Carbonell, Michael

PY - 2021

Y1 - 2021

N2 - The world is currently experiencing a major pandemic with the SARS-CoV-2 virus in which many patients who suffer and have suffered from this disease are more likely to suffer from hypertension. For this purpose, we have carried out a review of the scientific literature, from which we have collected 105 articles obtained from the following databases: ProQuest, Dialnet, ScienceDirect, Scopus, IEEE Xplore. Subsequently, based on the inclusion and exclusion criteria, 68 articles were systematized, detailing that Machine Learning helps us in the detection and prediction of hypertension in patients with coronavirus, Likewise, the predictive models that allow better detection of hypertension in patients with Covid 19 are 'Neural Networks', 'Cox Risk Model', 'Random Forest' and 'XGBoost', detailing the countries and technologies used.

AB - The world is currently experiencing a major pandemic with the SARS-CoV-2 virus in which many patients who suffer and have suffered from this disease are more likely to suffer from hypertension. For this purpose, we have carried out a review of the scientific literature, from which we have collected 105 articles obtained from the following databases: ProQuest, Dialnet, ScienceDirect, Scopus, IEEE Xplore. Subsequently, based on the inclusion and exclusion criteria, 68 articles were systematized, detailing that Machine Learning helps us in the detection and prediction of hypertension in patients with coronavirus, Likewise, the predictive models that allow better detection of hypertension in patients with Covid 19 are 'Neural Networks', 'Cox Risk Model', 'Random Forest' and 'XGBoost', detailing the countries and technologies used.

KW - Hypertension

KW - Machine learning

KW - Systematic review

UR - https://www.scopus.com/pages/publications/85124154461

U2 - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110

DO - 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110

M3 - Conference contribution

AN - SCOPUS:85124154461

T3 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

SP - 769

EP - 775

BT - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

T2 - 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021

Y2 - 30 September 2021 through 3 October 2021

ER -

Herrera-Huisa L, Arias-Meza N, Cabanillas-Carbonell M. Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature. In 19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021. 2021. p. 769-775. (19th IEEE International Symposium on Parallel and Distributed Processing with Applications, 11th IEEE International Conference on Big Data and Cloud Computing, 14th IEEE International Conference on Social Computing and Networking and 11th IEEE International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2021). doi: 10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00110

Analysis of the use of Machine Learning in the detection and prediction of hypertension in COVID 19 patients. A review of the scientific literature

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this