糖尿病人菜品AI推荐数据

糖尿病人菜品AI推荐数据的应用场景主要体现在为糖尿病患者、医疗专业人员以及餐饮服务商提供智能化、精准化的菜品推荐服务。通过分析糖尿病患者的基本信息和饮食习惯，AI模型能够为每个患者推荐符合其营养需求和饮食限制的菜品，有助于丰富糖尿病患者的就餐选择。对于医疗专业人员而言，通过本数据可以更好地了解患者的饮食习惯和营养摄入情况，从而提供更科学的饮食建议。餐饮服务商也能通过这些数据来调整菜单，确保菜品的多样性和营养均衡，满足不同糖尿病患者的需求。1.数据收集和预处理：（1）从公司订单系统抽取用户ID、抽取时间、人群类别、年龄、性别、健康状况、饮食习惯。（2）通过数据清洗去除无效或错误记录，确保数据质量。
2.特征生成：根据人群类别、年龄、性别、健康状况、饮食习惯，使用Feature-engine工具进行特征转换，生成特征标签。
3.实时预测：运用经公司自行训练和部署的基于深度交叉网络（DCN）深度学习架构的糖尿病人菜品智能推荐模型，根据生成的特征标签，对菜品进行实时预测和推荐。
4.结果解释：利用SHAP方法对推荐菜品进行解释，确保结构对用户的可理解性和可解释性。
5.评价优化：收集用户对推荐菜品的反馈，利用反馈数据对模型进行进一步的迭代和优化。

The application scenarios of the AI dish recommendation dataset for diabetic patients mainly focus on providing intelligent and precise dish recommendation services for diabetic patients, medical professionals and catering service providers. By analyzing the basic information and dietary habits of diabetic patients, the AI model can recommend dishes that meet their nutritional requirements and dietary restrictions for each individual patient, which effectively enriches the dining choices of diabetic patients. For medical professionals, this dataset enables them to gain a better understanding of patients' dietary habits and nutritional intake, so as to deliver more scientific dietary advice. Catering service providers can also leverage this data to adjust their menus, ensuring the diversity and nutritional balance of dishes to meet the needs of different diabetic patients.

1. Data Collection and Preprocessing:
(1) Extract user ID, extraction time, population category, age, gender, health status and dietary habits from the company's order system.
(2) Conduct data cleaning to remove invalid or erroneous records and guarantee data quality.
2. Feature Generation: Perform feature transformation using the Feature-engine tool based on the collected information including population category, age, gender, health status and dietary habits, to generate feature labels.
3. Real-time Prediction: Deploy the intelligent recommendation model for diabetic dishes, which is independently trained and deployed by the company and adopts the Deep & Cross Network (DCN) deep learning architecture, to carry out real-time prediction and recommendation of dishes based on the generated feature labels.
4. Result Explanation: Apply the SHAP method to explain the recommended dishes, ensuring that the recommendation results are comprehensible and interpretable for users.
5. Evaluation and Optimization: Collect user feedback on the recommended dishes, and utilize the feedback data to further iterate and optimize the recommendation model.

搜集汇总

数据集介绍

特点

该数据集是一个针对糖尿病患者的菜品AI推荐数据，包含582条记录，每日更新。数据集通过DCN模型分析用户的基本信息和饮食习惯，为糖尿病患者提供个性化的菜品推荐服务，适用于医疗专业人员和餐饮服务商。