Abstract

Obesity is a global health crisis linked to numerous chronic diseases and significant economic burdens. Traditional approaches to obesity management often struggle with personalization and long-term effectiveness. In recent years, Machine Learning (ML) has emerged as a powerful tool to innovate and improve obesity interventions by enabling personalized, data-driven solutions. This review article synthesizes current research on the application of ML techniques in understanding, predicting, and managing obesity. We examine studies that employ ML to analyse big data sets, from genomic information to lifestyle habits, creating models that predict obesity risk and the efficacy of specific interventions. Further, we explore ML-driven technologies, such as wearable devices and mobile applications, that support behavioural modifications essential for weight management. The review also discusses the integration of ML into clinical practices, including personalized dietary and physical activity recommendations, and the development of automated systems for continuous patient monitoring and support. Challenges such as data privacy, ethical considerations, and the need for interdisciplinary collaboration are addressed. Finally, future directions for ML in combating obesity are outlined, emphasizing the need for robust, scalable models that can be generalized across diverse populations. This article aims to provide a critical overview of the potential and limitations of ML in transforming obesity management and highlights how these technologies can lead to more effective and sustainable health outcomes.

Keywords:Obesity; Weight management; Machine learning; Diabetes; Health interventions