DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework

TL;DR

DeepHealthNet is a deep learning-based system that predicts adolescent obesity using health data, enabling early intervention and personalized health feedback with high accuracy, even with limited data.

Contribution

The paper introduces DeepHealthNet, a novel deep learning framework that improves adolescent obesity prediction accuracy using data augmentation and gender-specific analysis.

Findings

Prediction accuracy of 0.8842 overall

Higher accuracy for boys (0.9320) than girls (0.9163)

Effective in early obesity detection and personalized feedback

Abstract

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.