Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review - Zhou, Xiaobei; Chen, Lei; Liu, Hui-Xin (Frontiers in Nutrition, 2022)
Research on obesity and related diseases has received attention from government policymakers, interventions targeting nutrient intake, dietary patterns and physical activity are deployed globally. An urgent issue now is how can we improve the efficiency of obesity research or obesity interventions. Currently, machine learning methods have been widely applied in obesity-related studies to detect obesity-disease-biomarkers or discover the intervention strategies to optimize the weight loss results. In addition, open source of these algorithms is necessary to check the reproducibility of the research results. Furthermore, appropriate applications of these algorithms could greatly improve the efficiency of similar studies of other researchers. Here, we proposed a mini-review of several open-source machine learning algorithms, platforms or related databases that are of particular interest or can be applied in the field of obesity research., we focus our topic on nutrition, environment and social factors, genetics or genomics and microbiome adopting machine learning algorithms.