基于数据分析的久坐人群健康问题调查研究

A Survey on Health Issues of Sedentary Populations Based on Data Analysis

摘要: 久坐等不良生活方式引发的健康问题日益严峻，全球每年约160万人因此死亡。本文基于对久坐人群的健康调查数据，系统分析久坐行为与健康指标间的关联，并构建融合K-means聚类、分段函数建模、随机森林与梯度提升树的集成模型，实现人群健康状态分型、风险阈值判定与亚健康预测。研究结果为构建面向久坐人群的主动健康干预体系提供了方法与数据支撑，推动健康管理模式从“被动医疗”向“主动预防”转型，对提升公众健康水平具有积极意义。

Abstract: Health issues stemming from unhealthy lifestyles such as prolonged sitting are becoming increasingly severe, causing approximately 1.6 million deaths globally each year. Based on health survey data from sedentary populations, this study systematically analyzes the relationship between sedentary behavior and health indicators, and constructs an integrated model combining K-means clustering, piecewise function modeling, Random Forest, and Extreme Gradient Boosting to achieve health status classification, risk threshold identification, and sub-health prediction. The research outcomes provide methodological and data support for establishing an active health intervention system for sedentary populations, promoting a transition in health management from “passive treatment” to “active prevention” and contributing to the improvement of public health.