基于个人体质的膳食推荐指数数据

通过综合分析个人的基础数据，以及对用户体质的诊断分析，利用专有算法计算出个性化的膳食推荐指数，为用户提供科学的饮食建议。
根据该指数了用户的饮食需求，从而为用户制定出适合的健康饮食计划。

本数据适用于：
养老机构：根据每位老人的身体状况和健康需求，提供定制化的饮食建议。
研究机构：大学、研究所等科研机构可以利用这些数据知识产权进行营养学研究或临床试验。通过本公司中医诊断产品，完成对用户体质的诊断；将数据预处理后，输入到随机森林模型中，得出膳食推荐指数。
随机森林模型是通过构建多棵决策树并综合其预测结果。每棵决策树会根据身体指标特征重要性和分裂规则，对数据进行逐步的节点分裂，直至达到叶节点。
1.节点分裂：在每个内部节点，决策树根据某个身体指标特征向量及其阈值对数据进行分裂，选择信息增益最大的身体指标特征点和阈值。计算公式为：
Information Gain = H(S) - Σ (v ∈ Values(A)) |S_v| / |S| * H(S_v)
其中，是样本集 S 的熵，A 是特征，是特征 A 取值为时的样本子集。
2.综合多棵决策树的预测结果，最终的膳食推荐指数公式为：
(1/T) * ∑(从 t=1 到 T) y_t^(X)
其中，T是决策树的数量，y是第t棵决策树的预测值。X是输入的身体指标特征向量（身高、体重、年龄、口味偏好、过敏原、中医体质）。

By comprehensively analyzing individual basic data and the diagnostic analysis of the user's physical constitution, a proprietary algorithm is utilized to calculate a personalized dietary recommendation index, providing users with scientific dietary advice. Based on this index to determine the user's dietary needs, an appropriate healthy diet plan is formulated for the user.

This dataset is applicable to:
1. Elderly care institutions: Provide customized dietary advice based on the physical condition and health needs of each elderly resident.
2. Research institutions: Universities, research institutes and other scientific research organizations can utilize these data and their intellectual property rights to conduct nutritional research or clinical trials.

The user's physical constitution can be diagnosed through the company's traditional Chinese medicine (TCM) diagnostic products; after data preprocessing, the processed data is input into a random forest model to derive the dietary recommendation index.

A random forest model is constructed by building multiple decision trees and integrating their prediction results. Each decision tree performs gradual node splitting on the data based on the feature importance of physical indicators and splitting rules, until reaching leaf nodes.
1. Node splitting: At each internal node, the decision tree splits the data according to a specific physical indicator feature vector and its threshold, selecting the physical indicator feature point and threshold that yield the maximum information gain. The calculation formula is:
Information Gain = H(S) - Σ (v ∈ Values(A)) [|S_v| / |S|] * H(S_v)
Where H(S) denotes the entropy of the sample set S, A represents the feature, and S_v is the sample subset where feature A takes the value v.
2. Combining the prediction results of multiple decision trees, the final formula for the dietary recommendation index is:
(1/T) * ∑(t=1 to T) y_t(X)
Where T is the number of decision trees, y_t is the prediction value of the t-th decision tree, and X is the input physical indicator feature vector (including height, weight, age, taste preferences, allergens, and TCM constitution).

搜集汇总

数据集介绍

特点

该数据集是基于个人体质特征（如身高、体重、年龄等）和中医体质诊断，通过随机森林模型计算个性化膳食推荐指数的企业数据。每日更新，规模为1001条，适用于养老机构和研究机构，旨在提供科学的个性化饮食建议。