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

• Published in: Life (Basel, Switzerland) Vol. 12; no. 9; p. 1414
• Main Authors: Alsareii, Saeed Ali, Shaf, Ahmad, Ali, Tariq, Zafar, Maryam, Alamri, Abdulrahman Manaa, AlAsmari, Mansour Yousef, Irfan, Muhammad, Awais, Muhammad
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 10-09-2022; MDPI
• Subjects: Adults; Age; Algorithms; Artificial intelligence; Bayesian analysis; Body fat; Body mass index; Body weight; Chronic illnesses; classification; COVID-19; Datasets; Decision making; Decision trees; Developmental stages; Diabetes; Electronic health records; Food; Generalized linear models; Impact prediction; Internet of Things; IoT; Lifestyles; Machine learning; Morbidity; Obesity; Overweight; pandemic; Pandemics; real-time system; regression; Regression analysis; Regression models; Risk analysis; Risk factors; Support vector machines; Womens health; Saudi Arabia; Riyadh Saudi Arabia
• ISSN: 2075-1729; 2075-1729
• DOI: 10.3390/life12091414

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.