Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients

Based on the cardiovascular and digestive problems of obese patients, this paper adopted the cloud computing method and selected 100 subjects with big data (23 normal weight subjects, 3740 overweight patients, and 40 obese patients) as the research objects, studying the heart configuration and their...

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Detalles Bibliográficos
Autores principales: Du, ZhenZhong, Hu, Xin, Wu, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490022/
https://www.ncbi.nlm.nih.gov/pubmed/34616533
http://dx.doi.org/10.1155/2021/4695722
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author Du, ZhenZhong
Hu, Xin
Wu, Jin
author_facet Du, ZhenZhong
Hu, Xin
Wu, Jin
author_sort Du, ZhenZhong
collection PubMed
description Based on the cardiovascular and digestive problems of obese patients, this paper adopted the cloud computing method and selected 100 subjects with big data (23 normal weight subjects, 3740 overweight patients, and 40 obese patients) as the research objects, studying the heart configuration and their digestive system of obese people. Results show that BMI = L (24 ≥ BMI > 27.9) and BMI = XL (BMI > 27.9) were identified as target correlation projects in this experiment, associated with each cardiac structural parameters, respectively. Cloud computing facilitates early detection, early prevention, and early intervention in heart configuration changes in overweight and obese patients.
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spelling pubmed-84900222021-10-05 Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients Du, ZhenZhong Hu, Xin Wu, Jin J Healthc Eng Research Article Based on the cardiovascular and digestive problems of obese patients, this paper adopted the cloud computing method and selected 100 subjects with big data (23 normal weight subjects, 3740 overweight patients, and 40 obese patients) as the research objects, studying the heart configuration and their digestive system of obese people. Results show that BMI = L (24 ≥ BMI > 27.9) and BMI = XL (BMI > 27.9) were identified as target correlation projects in this experiment, associated with each cardiac structural parameters, respectively. Cloud computing facilitates early detection, early prevention, and early intervention in heart configuration changes in overweight and obese patients. Hindawi 2021-09-27 /pmc/articles/PMC8490022/ /pubmed/34616533 http://dx.doi.org/10.1155/2021/4695722 Text en Copyright © 2021 ZhenZhong Du et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Du, ZhenZhong
Hu, Xin
Wu, Jin
Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients
title Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients
title_full Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients
title_fullStr Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients
title_full_unstemmed Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients
title_short Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients
title_sort application of cloud computing in the prediction of exercise improvement of cardiovascular and digestive systems in obese patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8490022/
https://www.ncbi.nlm.nih.gov/pubmed/34616533
http://dx.doi.org/10.1155/2021/4695722
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