AI Summary & Key Takeaways
AI InsightPerformance evaluation of models is compared in terms of accuracy, Fleiss's kappa, classification error, and absolute error.
📌 3 Key Takeaways
Abstract
Obesity occurs as a result of excessive fat storage in the body and brings along physical and mental problems [1]. The physical function has been associated with impaired quality of life in various areas such as distress in society, sexual function, self-esteem, and work-related quality of life [2]. The prevalence of obesity has been steadily increasing over the past few decades and is now unprecedented. This increase has occurred in almost all ages, genders, and races. These data show that the segments of individuals in the highest weight categories i.e. (BMI> 40 kg / m2) increased proportionally more than those in the lower BMI categories (BMI <35 kg / m2) [3]. Given the numerous and important health consequences associated with obesity, there is an urgent need to develop highly effective interventions aimed at reversing these “obesogenic” drivers, including both government policies and health education and development programs. It is important to implement measures to be taken, including both government policies and health education and development programs, especially during the COVID-19 pandemic process we are in. In this study, the data set on the open-source access website was used for the prediction of obesity levels and consists of patient records of 17 variables created by the deep learning repository. In addition, the performance of deep learning methods in the prediction of obesity levels was examined and determined. Performance evaluation of models is compared in terms of accuracy, Fleiss's kappa, classification error, and absolute error.