Adult Obesity

Adult obesity research focuses on understanding its complex etiology and developing effective interventions. Current studies utilize machine learning techniques, including social network analysis, variational autoencoders, and various regression models, to identify risk factors (e.g., socioeconomic status, dietary habits, social networks) and predict individual nutrient needs or disease risk based on BMI trajectories and other physiological data. These analyses aim to improve obesity classification, personalize dietary recommendations, and ultimately inform public health strategies to mitigate the significant health and socioeconomic burdens associated with this prevalent condition.