云南养老院健康小屋身体健康指数数据
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通过采集健康小屋平台上云南养老院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： 养老院：为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，采集云南养老院的数据，完成对云南养老院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）。
By collecting daily health screening data of users served by Yunnan nursing homes on the Health Cabin platform, we conduct cardiac health assessment and cardiovascular disease assessment, and take factors including body fat percentage, basal metabolism, blood viscosity, fatigue degree, emotional stress and others as independent variables to comprehensively analyze the individual's health index. The Health Cabin 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: Nursing homes: Provide one-stop health management solutions for the elderly, including daily monitoring, disease management and rehabilitation training, and provide users with personalized diet and exercise recommendations based on the index. Through the company's Health Cabin platform, data from Yunnan nursing homes is collected to complete health detection and assessment for the service objects of Yunnan nursing homes; after data preprocessing, the data is input into the Random Forest model, and the dietary recommendation index is calculated based on the score values of each factor level in the model. By integrating the prediction results of multiple decision trees, the final physical health index formula is: = (1/N) * ∑(i=1 to N) Ti(x) Where N is the number of decision trees in the Random Forest; Σ(i=1 to N) represents the summation from 1 to N; Ti(x) is the predicted 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 percentage, basal metabolism, blood viscosity, emotional stress, fatigue degree, cardiovascular disease assessment).
