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...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Online Access: | https://www.mdpi.com/2075-1729/12/9/1414 |
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| author | Saeed Ali Alsareii Ahmad Shaf Tariq Ali Maryam Zafar Abdulrahman Manaa Alamri Mansour Yousef AlAsmari Muhammad Irfan Muhammad Awais |
| author_facet | Saeed Ali Alsareii Ahmad Shaf Tariq Ali Maryam Zafar Abdulrahman Manaa Alamri Mansour Yousef AlAsmari Muhammad Irfan Muhammad Awais |
| author_sort | Saeed Ali Alsareii |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T23:22:14Z |
| format | Article |
| id | doaj.art-318320e6c2dc4f44a32d59198bc20dd7 |
| institution | Directory Open Access Journal |
| issn | 2075-1729 |
| language | English |
| last_indexed | 2024-03-09T23:22:14Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj.art-318320e6c2dc4f44a32d59198bc20dd72023-11-23T17:23:43ZengMDPI AGLife2075-17292022-09-01129141410.3390/life12091414IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and AdultsSaeed Ali Alsareii0Ahmad Shaf1Tariq Ali2Maryam Zafar3Abdulrahman Manaa Alamri4Mansour Yousef AlAsmari5Muhammad Irfan6Muhammad Awais7Department of Surgery, College of Medicine, Najran University Saudi Arabia, Najran 11001, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, PakistanDepartment of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, PakistanDepartment of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, PakistanDepartment of Surgery, College of Medicine, Najran University Saudi Arabia, Najran 11001, Saudi ArabiaDepartment of Surgery, College of Medicine, Najran University Saudi Arabia, Najran 11001, Saudi ArabiaElectrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 11001, Saudi ArabiaDepartment of Computer Science, Edge Hill University, St Helens Rd, Ormskirk L39 4QP, UKApproximately 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.https://www.mdpi.com/2075-1729/12/9/1414IoTpandemicobesityclassificationregressionreal-time system |
| spellingShingle | Saeed Ali Alsareii Ahmad Shaf Tariq Ali Maryam Zafar Abdulrahman Manaa Alamri Mansour Yousef AlAsmari Muhammad Irfan Muhammad Awais IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults Life IoT pandemic obesity classification regression real-time system |
| 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 | IoT pandemic obesity classification regression real-time system |
| url | https://www.mdpi.com/2075-1729/12/9/1414 |
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