模糊建模实现个性化营养菜单

K. E. El Moutaouakil, A. Ahourag, Fatima Belhabib, Aayah Hammoumi, A. Patriciu, S. Chellak, Hicham Baizri
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引用次数: 0

摘要

虽然大多数健康饮食有助于控制疾病的发展,但从长远来看,它们可能会因多种因素而失败。患者会因为饮食限制过多或食物不可口而在一段时间后完全放弃饮食;还有一些患者会因为摄入的卡路里较少而减少运动量。更重要的是,几乎所有计划都基于优化模型。这些模型产生的统计饮食为用户提供了有限的选择,一个小的替代品就可能使整个饮食受到质疑。本文开发了一个智能系统,用于生成灵活的营养计划,让每个人都能根据自己的环境和饮食偏好(食物的可获得性、价格、病人的饮食习惯等)进行选择。目的:本文基于数学建模和约束满足编程建立了个性化营养菜单。首先,使用模糊 Cmeans 对摩洛哥食品进行了分解,然后将由中心形成的人工食品引入模糊数学优化饮食模型,该模型控制了总血糖负荷,并符合世界卫生组织(WHO)和《美国人膳食指南》(GDA)的建议(个性化菜单的要求)。然后,我们使用遗传算法策略生成最佳配餐量,并根据所形成的组别构建营养菜单。为了帮助患者选择定制饮食,我们将菜单转化为约束满足编程模型。建议的策略已应用于摩洛哥食品,实验结果表明,所有饮食都为用户提供了广泛的选择,并且替代品符合世界卫生组织和美国膳食指南的建议。
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Fuzzy Modeling to Personalized Nutritional Menu
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. 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. 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. Objective: this paper builds a personalized nutritional menu based on mathematical modeling and constraints satisfaction programming. 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. 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. 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.
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