山西月子中心健康小屋身体健康指数数据

通过采集健康小屋平台上山西月子中心所服务用户的日常健康检测，完成对用户心脏健康评估，心血管疾病评估、体脂率、基础代谢、血液粘稠度、疲劳度、情绪压力等因素为自变量，综合分析个人的健康指数，健康小屋帮助用户更好地了解自己的健康状况，及时发现潜在问题，并提供个性化的健康建议和支持。
本数据适用于：
月子中心：为客户提供了一站式的健康管理解决方案，无论是日常监测、疾病管理还是康复训练，通过该指数为用户提供个性化的饮食和运动的建议。通过本公司健康小屋平台，采集山西月子中心的数据，完成对山西月子中心服务对象的健康检测和评估；将数据预处理后，输入到随机森林模型中，根据模型中各因素水平的分值得出膳食推荐指数。
综合多棵决策树的预测结果，最终的身体健康指数公式为：
= (1/N) * ∑(i=1 to N) Ti(x)

N 是随机森林中决策树的数量；
Σ(i=1 to N) 表示从1到N的求和；
Ti(x) 是第i个决策树对输入x的预测输出；
X是输入的身体指标特征向量（身高、体重、年龄、性别、心率、体脂率、基础代谢、血液粘稠度、情绪压力、疲劳度、心血管疾病评估）。

This dataset is developed by collecting routine health examination data of users served by Shanxi postpartum care centers via the Health Cabin platform. Taking factors including cardiac health assessment, cardiovascular disease assessment, body fat rate, basal metabolism, blood viscosity, fatigue level, emotional stress and other indicators as independent variables, it comprehensively analyzes the individual's health index. The Health Cabin platform helps users better understand their own health status, detect potential problems in a timely manner, and provide personalized health advice and support.

This dataset is applicable to postpartum care centers: it provides customers with one-stop health management solutions, including personalized diet and exercise recommendations based on the health index for scenarios such as routine monitoring, disease management and rehabilitation training. Data of Shanxi postpartum care centers are collected via the company's Health Cabin platform to complete health detection and assessment for the service recipients of these centers. After data preprocessing, the data are input into the Random Forest model, and the diet recommendation index is calculated based on the scores of each factor level in the model.

The final physical health index formula, which integrates the prediction results of multiple decision trees, is:
$$ ext{PHI} = frac{1}{N} sum_{i=1}^{N} T_i(x)$$
Where:
- $N$ is the number of decision trees in the Random Forest;
- $sum_{i=1}^{N}$ denotes the summation from 1 to N;
- $T_i(x)$ is the prediction output of the $i$-th decision tree for input $x$;
- $X$ is the input physical indicator feature vector (height, weight, age, gender, heart rate, body fat rate, basal metabolism, blood viscosity, emotional stress, fatigue level, cardiovascular disease assessment).

搜集汇总

数据集介绍

特点

该数据集包含山西月子中心用户的健康检测数据，涵盖多项身体指标，用于评估和改善用户的健康状况，适用于月子中心的健康管理服务。