Chronic Diseases: A Comprehensive Review of Applications for Prediction of Obesity

Abstract

Recent advancements in ML have opened new avenues for addressing complex health issues by facilitating personalized medicine, predictive diagnostics, and behaviour modification strategies. In the context of obesity, ML algorithms can analyse vast datasets-from genetic predispositions to behavioural and environmental factors-enabling the development of tailored intervention strategies that are more adaptive and responsive to individual needs [9,10]. Moreover, ML can enhance the real-time monitoring and management of obesity through wearable technology and mobile applications, offering immediate feedback and support to individuals as they navigate their daily choices [11,12]. The integration of ML into obesity research and management is not without challenges [13,14]. Issues such as data privacy, algorithmic bias, and the digital divide pose significant barriers to the widespread adoption of these technologies [7,15]. Moreover, the effectiveness of ML-driven interventions must be scrutinized through rigorous, multidisciplinary research to ensure they deliver