With the change in their social environment and life patterns, the eating habits of modern people have become more diverse. Eating habits are closely related to health, and diet management for individual healthcare is required in this era characterized by poor health and increased longevity. In this paper, we propose a knowledge-based hybrid decision model for nutrition management that uses neural networks. The proposed method is a food recommendation model to help users make dietary nutrition-related decisions on a health platform. It is a hybrid recommendation method that considers both physical and mental health. It selects foods that are positively related to users’ physical health as candidates and predicts users’ preferences through adaptive learning. A previously developed dietary nutrition service ontology is used to select foods that appear to affect the user’s health positively. Conventional preference prediction methods include collaborative, content-based, knowledge-based, and image-based filtering. These methods use a hybrid model or machine learning, data mining, and artificial intelligence methods to compensate for the disadvantages of each filtering type. For preference prediction, healthcare and food preference data are collected in an on/off line environment. The data consist of age, sex, body mass index, region, chronic disease, and food preferences. Food preferences include the dietary nutritional components of food, which makes it possible to infer the user’s preferences for foods containing calories, carbohydrates, protein, fat, sugars, sodium, cholesterol, saturated fatty acids, and trans fatty acids. The user’s preference for food is composed of output variables, and other variables are composed of input variables. The variables consist of 11 healthcare data variables, 2 preference data variables, 10 dietary nutrition data variables, 22 input variables, and 1 output variable. The variables that we constructed are used to arrange transactions and supervised learning is conducted in a neural network structure. In total, 3152 transactions, 80% of the collected data, were used as learning data and 788 transactions, 20% of the collected data, as test data. Using the test data, we evaluated the performance of four prediction models based on a learned neural network, user correlation, average replacement, and regression analysis, respectively. The result of the performance evaluation showed that the proposed method was superior to the conventional method in that it solved the cold-start and the sparsity problem. In addition, the user’s satisfaction evaluation result was 3.92 on a five-point scale, showing overall satisfaction. Therefore, on the platform it is possible to recommend dietary nutrition for people suffering chronic diseases according to their lifestyle and in consideration of their health status and preferences. The platform selects a suitable candidate food according to the health condition of the user and provides a recommendation for N foods using the Top-N of the user’s food preferences.

中文翻译：

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基于知识的神经网络营养管理混合决策模型

随着他们的社会环境和生活方式的改变，现代人的饮食习惯变得更加多样化。饮食习惯与健康息息相关，在这个时代，健康状况不佳，寿命延长是个人饮食管理所必需的。在本文中，我们提出了使用神经网络的基于知识的营养管理混合决策模型。所提出的方法是一种食物推荐模型，可以帮助用户在健康平台上做出与饮食营养有关的决定。这是一种混合建议方法，同时考虑了身体和精神健康。它选择与用户身体健康正相关的食品作为候选食品，并通过适应性学习来预测用户的喜好。先前开发的饮食营养服务本体用于选择似乎对用户健康产生积极影响的食物。常规的偏好预测方法包括协作，基于内容，基于知识和基于图像的过滤。这些方法使用混合模型或机器学习，数据挖掘和人工智能方法来弥补每种过滤类型的缺点。对于偏好预测，在在线/离线环境中收集医疗保健和食物偏好数据。数据包括年龄，性别，体重指数，区域，慢性疾病和食物偏好。食物的偏爱包括食物的饮食营养成分，这可以推断出用户对含有卡路里，碳水化合物，蛋白质，脂肪，糖，钠，胆固醇，饱和脂肪酸和反式脂肪酸。用户对食物的偏爱由输出变量组成，其他变量由输入变量组成。这些变量包括11个医疗保健数据变量，2个偏好数据变量，10个饮食营养数据变量，22个输入变量和1个输出变量。我们构造的变量用于安排交易，并且在神经网络结构中进行监督学习。总共将3152笔交易（收集的数据的80％）用作学习数据，将788笔交易（收集的数据的20％）用作测试数据。使用测试数据，我们分别基于学习的神经网络，用户相关性，平均替换和回归分析，评估了四个预测模型的性能。性能评估结果表明，该方法解决了冷启动和稀疏问题，优于常规方法。另外，用户的满意度评价结果在5分制中为3.92分，显示整体满意度。因此，在该平台上，可以根据生活方式和健康状况和喜好为患有慢性疾病的人们推荐饮食营养。该平台根据用户的健康状况选择合适的候选食物，并使用用户食物偏好的前N个为N种食物提供推荐。五分制得分为92，表明总体满意度。因此，在该平台上，可以根据生活方式和健康状况和喜好为患有慢性疾病的人们推荐饮食营养。该平台根据用户的健康状况选择合适的候选食物，并使用用户食物偏好的前N个为N种食物提供推荐。五分制得分为92分，显示整体满意度。因此，在该平台上，可以根据生活方式和健康状况和喜好为患有慢性疾病的人们推荐饮食营养。该平台根据用户的健康状况选择合适的候选食物，并使用用户食物偏好的前N个为N种食物提供推荐。