黑龙江老年服务站健康小屋身体健康指数数据

通过采集健康小屋平台上黑龙江老年服务站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： 老年服务站：为社区老人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，采集黑龙江老年服务站的数据，完成对黑龙江老年服务站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 constructed by collecting routine health monitoring data of users served by Heilongjiang Elderly Service Stations through the Healthy Hut platform. Taking multiple indicators including cardiac health assessment, cardiovascular disease assessment, body fat percentage, basal metabolic rate, blood viscosity, fatigue level and emotional stress as independent variables, it conducts comprehensive analysis to generate individual health index. The Healthy Hut platform enables users to gain a clearer understanding of their health conditions, identify potential issues promptly, and obtain personalized health advice and support. This dataset is applicable to the following scenarios: Elderly Service Stations: It provides one-stop health management solutions for community seniors, covering routine health monitoring, disease management and rehabilitation training, and delivers personalized dietary and exercise recommendations to users based on the generated health index. Data of Heilongjiang Elderly Service Stations is collected via the company's Healthy Hut platform to complete health examinations and assessments for their service recipients. After preprocessing, the dataset is fed into a random forest model, and the dietary recommendation index is calculated based on the score values of each factor level within the model. By aggregating the prediction outputs of multiple decision trees, the final physical health index is calculated using the following formula: $$ ext{Health Index} = frac{1}{N} sum_{i=1}^{N} T_i(x)$$ Where: - $N$: the total number of decision trees in the random forest; - $sum_{i=1}^{N}$: the summation operation from $i=1$ to $i=N$; - $T_i(x)$: the prediction output of the $i$-th decision tree for input feature vector $x$; - $X$: the input physical indicator feature vector, including height, weight, age, gender, heart rate, body fat percentage, basal metabolic rate, blood viscosity, emotional stress, fatigue level and cardiovascular disease assessment.