随机森林和特征重要性测量用于区分最有影响力的环境因素,预测心血管和呼吸系统疾病
Francesco Cappelli1, Gianfranco Castronuovo2, Salvatore Grimaldi1
1DIBAF Department, University of Tuscia, 01100 Viterbo, Italy.
概括
环境和气候因素对心血管和呼吸系统疾病产生重大影响. 这项研究确定温度,大气压和一氧化碳是关键预测因素,有助于公共卫生政策和预警系统.
科学领域:
- 环境健康 环境健康
- 生物统计学 生物统计学
- 公共卫生 公共卫生
背景情况:
- 环境和气候因素与心血管和呼吸道疾病的死亡率有关.
- 确定最有影响力的因素仍然是一个挑战.
- 本研究采用数据驱动的统计方法进行分析.
研究的目的:
- 分析心血管和呼吸系统疾病的每日急诊室入院情况.
- 确定影响这些入口的最有影响力的环境和气候参数.
- 证明数据驱动的统计方法在环境健康研究中的实用性.
主要方法:
- 使用的随机森林 (RF) 模型和特征重要性测量 (PFI,SHAP, κALE).
- 分析了每日摄入量以及环境/气候数据 (温度,压力,湿度,CO,O3,PM,NO2).
- 预处理数据使用季节趋势分解 (STL) 来删除趋势和季节性.
主要成果:
- 射频模型在预测疾病入院的准确性很高 (MAE:0.04为心血管,0.05为呼吸道).
- 功能重要性技术确定了关键的有影响力的参数.
- 发现温度,大气压和一氧化碳是最重要的预测因素.
结论:
- 数据驱动的统计工具,如特征重要性指标,在识别环境风险因素方面是有效的.
- 结果支持将这些工具用于公共卫生政策,包括对气候变化相关健康风险的早期预警系统.
- 这些发现可以改善心血管和呼吸系统疾病的疾病预防策略.
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