Fuzzy Modeling to Personalized Nutritional Menu

Abstract

Background: While most healthy diets can help control the progression of disease, they can fail in the long term for many factors. Patients abandon the diet altogether after a while because it is too restrictive or the foods are unappealing; still, others engage in less physical activity because they consume fewer calories. What's more, almost all plans are based on optimization models. These models produce statistical diets offering limited choices to users, and a small substitution can call the whole diet into question. Objective: This article develops an intelligent system for generating flexible nutritional menus that each person can adopt to their environment and dietary preferences (food availability, price, patient eating habits, etc.). The system implements mathematical fuzzy optimization models and constraint satisfaction programming. Methods and Materials: First, the Moroccon foods were decomposed using fuzzy Cmeans. Next, the artificial foods, formed by the centers, were introduced into a fuzzy mathematical optimization diet model, which controlled the total glycemic load and met the World Health Organization (WHO) and Dietary Guidelines for Americans (GDA) recommendations (requirements for personalized menu). Then, we used a genetic algorithm strategy to generate optimal serving sizes and to build a nutritional menu based on the groups formed. To help patients choose customized diets, the menu was transformed into a constraint satisfaction programming model. Results: The proposed strategy was applied to Moroccan foods, experimental results show that all diets offer a wide range of choices to users and that substitutions comply with WHO and GDA recommendations. Conclusion: The suggested scheme has been applied to Moroccan foods; experimental findings demonstrate that all diets provide users with a wide variety of options that keeps consumers on their diet.