“三高”人群菜品AI推荐数据

“三高”人群菜品AI推荐数据的应用场景主要体现在为“三高”患者、医疗健康顾问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“三高”人群的基本信息和饮食习惯，AI模型能够为每位“三高”患者推荐符合其低盐、低糖、低脂肪等特定营养需求的菜品，有助于丰富“三高”患者的就餐选择。对于医疗健康顾问而言，通过本数据可以更好地了解“三高”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“三高”人群的需求。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“三高”人群菜品智能推荐模型，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 people with hypertension, hyperglycemia and hyperlipidemia (hereinafter referred to as the "three highs" population) mainly focus on providing intelligent and precise dish recommendation services for patients with the "three highs", medical health consultants and catering service providers.

By analyzing the basic information and dietary habits of the "three highs" population, the AI model can recommend dishes that meet their specific nutritional requirements such as low-salt, low-sugar and low-fat for each patient, which helps to enrich the dining options of these patients.

For medical health consultants, this dataset can enable them to gain a better understanding of the eating habits and nutritional intake of "three highs" patients, thereby providing 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 "three highs" groups.

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) Remove invalid or erroneous records via data cleaning to guarantee data quality.

2. Feature Generation: Conduct feature transformation using the Feature-engine tool based on the population category, age, gender, health status and dietary habits to generate feature labels.

3. Real-time Prediction: Deploy the self-developed and trained intelligent dish recommendation model for the "three highs" population based on the Deep & Cross Network (DCN) deep learning architecture, and perform real-time dish prediction and recommendation based on the generated feature labels.

4. Result Interpretation: Apply the SHAP method to interpret the recommended dishes, ensuring the comprehensibility and interpretability of the recommendation results for users.

5. Evaluation and Optimization: Collect user feedback on the recommended dishes, and use the feedback data to further iterate and optimize the model.

搜集汇总

数据集介绍

特点

“三高”人群菜品AI推荐数据是一个包含590条记录的企业数据集，每日更新，用于通过DCN模型为“三高”人群推荐符合其健康需求的菜品。该数据集支持医疗健康顾问和餐饮服务商提供更科学的饮食建议和菜单调整。