Food patterns and obesity prevalence across countries: A machine learning application
Abstract: Obesity is recognized as a global pandemic. Using standardized data from different countries, we have implemented a machine learning approach to identify food patterns associated with a higher obesity risk. We considered random forests to perform a binary classification of countries' obesity risk. The method has a 74% of accuracy. The variable importance list, an outcome of the method, identified food highly processed as important predictors of obesity at the country level, which has been recognized as one of the main driver of the nutrition transition.
Bio: Jocelyn Dunstan is a physicist by training, and applies that, and machine learning to public health.
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