IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults

Abstract

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.

Development of an Ingestible Expandable Capsule for Weight Loss

“…Persons with BMI of 30 or more are considered obese and persons with BMI equal to or more than 25 are considered overweight [ 1 ]. The estimated number of overweight and obese adults worldwide is 2.1 billion [ 2 ]. The global COVID-19 pandemic may also have accelerated the increase in obesity prevalence.…”

A Deep Dive into the Nexus between Digital Health and Life Sciences Amidst the COVID-19 Pandemic: An Editorial Expedition

“…All the other studies [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] have dealt with the use of DH in the life sciences, which, in addition to bioengineering, touched by all, have examined, from time to time, other categories ( Table 1 ), such as Anatomy [ 8 , 11 ], Bioinformatics [ 14 , 16 ], Cell biology [ 8 , 11 ], Neuroscience [ 8 , 12 , 15 , 17 , 20 ], Physiology [ 9 , 10 , 12 , 13 ], Population biology [ 14 , 16 , 18 , 19 ], and others that are shown in Table 1 .…”

“…The second study applied IoT and AI [ 10 ] in investigating the risk factors and the ratio of obesity, proposing an approach for obesity diagnosis in its initial stages, significantly increasing the patient’s chances of effective treatment.…”

“…The published works on the DH, reported in the collection [ 1 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ], demonstrate the usefulness of DH in connecting various branches of life sciences, especially during the pandemic. The use of DH in contact tracing has been particularly significant, with several studies reporting on its impact and factors influencing its use [ 2 , 4 , 5 , 6 , 7 ].…”

“…DH has effectively aided public health research related to studying factors affecting the health of populations, while also contributing to advances in various fields of the life sciences ( Table 1 ), such as, Biongineering [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ], Anatomy [ 8 , 11 ], Bioinformatics [ 14 , 16 ], Cell biology [ 8 , 11 ], Neuroscience [ 8 , 12 , 15 , 17 , 20 ], Physiology [ 9 , 10 , 12 , 13 ], and Population biology [ 14 , 16 , 18 ].…”