Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence

TL;DR

This study demonstrates how wearable devices combined with AI can effectively predict weight loss outcomes in overweight individuals, highlighting personalized healthcare potential.

Contribution

It introduces a novel approach using multi-source wearable data and machine learning to predict weight loss success in real-time.

Findings

Gradient Boosting classifier achieved 84.44% AUC.

Multi-source data improves prediction performance.

Key biomarkers and behavioral features distinguish weight loss success.

Abstract

Early detection of chronic and Non-Communicable Diseases (NCDs) is crucial for effective treatment during the initial stages. This study explores the application of wearable devices and Artificial Intelligence (AI) in order to predict weight loss changes in overweight and obese individuals. Using wearable data from a 1-month trial involving around 100 subjects from the AI4FoodDB database, including biomarkers, vital signs, and behavioral data, we identify key differences between those achieving weight loss (>= 2% of their initial weight) and those who do not. Feature selection techniques and classification algorithms reveal promising results, with the Gradient Boosting classifier achieving 84.44% Area Under the Curve (AUC). The integration of multiple data sources (e.g., vital signs, physical and sleep activity, etc.) enhances performance, suggesting the potential of wearable devices and AI in personalized healthcare.