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IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults
Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted ob...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500775/ https://www.ncbi.nlm.nih.gov/pubmed/36143450 http://dx.doi.org/10.3390/life12091414 |
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author | Alsareii, Saeed Ali Shaf, Ahmad Ali, Tariq Zafar, Maryam Alamri, Abdulrahman Manaa AlAsmari, Mansour Yousef Irfan, Muhammad Awais, Muhammad |
author_facet | Alsareii, Saeed Ali Shaf, Ahmad Ali, Tariq Zafar, Maryam Alamri, Abdulrahman Manaa AlAsmari, Mansour Yousef Irfan, Muhammad Awais, Muhammad |
author_sort | Alsareii, Saeed Ali |
collection | PubMed |
description | Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient’s chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others. |
format | Online Article Text |
id | pubmed-9500775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95007752022-09-24 IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults Alsareii, Saeed Ali Shaf, Ahmad Ali, Tariq Zafar, Maryam Alamri, Abdulrahman Manaa AlAsmari, Mansour Yousef Irfan, Muhammad Awais, Muhammad Life (Basel) Article Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient’s chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others. MDPI 2022-09-10 /pmc/articles/PMC9500775/ /pubmed/36143450 http://dx.doi.org/10.3390/life12091414 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Alsareii, Saeed Ali Shaf, Ahmad Ali, Tariq Zafar, Maryam Alamri, Abdulrahman Manaa AlAsmari, Mansour Yousef Irfan, Muhammad Awais, Muhammad IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults |
title | IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults |
title_full | IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults |
title_fullStr | IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults |
title_full_unstemmed | IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults |
title_short | IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults |
title_sort | iot framework for a decision-making system of obesity and overweight extrapolation among children, youths, and adults |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9500775/ https://www.ncbi.nlm.nih.gov/pubmed/36143450 http://dx.doi.org/10.3390/life12091414 |
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