基于随机森林回归的寒区作业人群健康预测分析

Health Prediction Analysis of Personnel in Cold Regions Based on Random Forest Regression

摘要: 为了解决寒区作业人群因特殊环境暴露导致的健康风险预测与防控策略不足的问题，本研究采用横断面研究设计，选取我国寒区地区(西藏、东北高纬度)作业人员，共纳入255名健康受试者，探索寒区作业人群健康影响因素。系统收集人口学特征、生活方式指标、健康结局数据及寒区环境参数。本研究基于国际临床指南和寒区医学专家咨询，构建了一个综合性的临床风险指数(CRI)作为连续性健康结局指标，该指数整合了血压、血糖、尿酸、血脂、症状及自评健康等多维信息。采用基于随机森林回归模型的预测方法，通过数据预处理、模型构建及评估，预测了寒区作业人群的临床风险指数。其结果对于制定寒区作业人员的健康保障措施、提高作业效率和安全性、以及推动寒区医学发展具有重要的理论和实践意义。

Abstract: In order to address the issue of insufficient health risk prediction and prevention strategies for workers in cold regions exposed to unique environmental conditions, this study employed a cross-sectional research design, selecting workers from Xizang, China, and including a total of 255 healthy participants to explore the health influencing factors of the cold region workforce. Systematic data was collected on demographic characteristics, lifestyle indicators, health outcome data, and environmental parameters of the cold region. Based on international clinical guidelines and consultations with cold medicine experts, a comprehensive Clinical Risk Index (CRI) was developed as a continuous health outcome indicator, which integrates multi-dimensional information such as blood pressure, blood sugar, uric acid, blood lipids, symptoms, and self-rated health. Using a prediction method based on a random forest regression model, the clinical risk index for workers in cold regions was predicted through data preprocessing, model construction, and evaluation. The results are of significant theoretical and practical importance for formulating health protection measures for cold region workers, improving work efficiency and safety, and promoting the development of cold medicine.