Machine Learning to Predict Overeating from Macronutrient Composition This paper investigates nutritional deficiencies as a possible cause of overeating and obesity. Existing medical research suggests such a connection. Data obtained from public calorie logs were used to create decision tree models and random forest classifiers that can predict instances of overeating based on macronutrient composition at varying overeating thresholds. Macronutrient composition was defined as the percentage of carbohydrate, protein, and fat calorie intake out of the total calorie intake. Our research indicates that macronutrient composition has a large role in predicting overeating lapses and could be used in current calorietracking applications to help users lose weight and adhere to healthy nutritional guidelines.

Automatic Estimation of the Nutritional Composition of Foods as Part of the GlucoseML Type 1 Diabetes SelfManagement System The daily care of type 1 diabetes has been considerably improved through the increased adoption of continuous glucose monitoring, continuous subcutaneous insulin infusion, and precise behavioral monitoring (diet, physical activity) mHealth solutions. In this study, we present the food recognition and nutrient estimation components of the GlucoseML system; a type 1 diabetes selfmanagement system relying on shortterm predictive analytics of the glucose trajectory. A computervisionbased approach is outlined combining image processing and machine learning to plate detection, food segmentation, food recognition and volume estimation of a plateu0027s content. The systematic collection of an annotated Greek food images dataset allows the evaluation of the proposed methodology.