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
Date Issued
01 January 2021
Access level
metadata only access
Resource Type
conference paper
Author(s)
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
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.
Start page
769
End page
775
Language
English
OCDE Knowledge area
Sistema cardiaco, Sistema cardiovascular
Ingeniería de sistemas y comunicaciones
Salud pública, Salud ambiental
Ciencias de la computación
Subjects
Scopus EID
2-s2.0-85124154461
ISBN of the container
9781665435741
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
Sources of information:
Directorio de Producción Científica
Scopus