  Childhood and adolescent obesity rates are a global concern because obesity
is associated with chronic diseases and long-term health risks. Artificial
intelligence technology has emerged as a promising solution to accurately
predict obesity rates and provide personalized feedback to adolescents. This
study emphasizes the importance of early identification and prevention of
obesity-related health issues. Factors such as height, weight, waist
circumference, calorie intake, physical activity levels, and other relevant
health information need to be considered for developing robust algorithms for
obesity rate prediction and delivering personalized feedback. Hence, by
collecting health datasets from 321 adolescents, we proposed an adolescent
obesity prediction system that provides personalized predictions and assists
individuals in making informed health decisions. Our proposed deep learning
framework, DeepHealthNet, effectively trains the model using data augmentation
techniques, even when daily health data are limited, resulting in improved
prediction accuracy (acc: 0.8842). Additionally, the study revealed variations
in the prediction of the obesity rate between boys (acc: 0.9320) and girls
(acc: 0.9163), allowing the identification of disparities and the determination
of the optimal time to provide feedback. The proposed system shows significant
potential in effectively addressing childhood and adolescent obesity.
