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
N1 - Publisher Copyright:
© 2021 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85124154461&partnerID=8YFLogxK
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 -
