基于随机森林模型的呼吸系统疾病预测
Xiaotong Yang1,2, Yi Li1,2, Lang Liu1,2
1College of Meteorology and Oceanography, National University of Defense Technology, Changsha, China.
Frontiers in public health
|March 3, 2025
概括
这项研究使用随机森林模型来分析空气污染和天气对天津呼吸系统医疗访问的影响. 该模型准确地预测了每日访问量,突出了温度和臭氧等关键影响因素.
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:
- 空气污染和气象因素显著影响呼吸系统健康.
- 了解这些关系对于公共卫生管理和资源分配至关重要.
- 之前的研究已经探索了这些联系,但需要先进的建模才能准确预测.
研究的目的:
- 为了调查天津2013-2019年呼吸系统疾病医疗访问的时间模式.
- 用随机森林模型确定这些访问的主要影响因素 (空气污染,气象).
- 开发和验证呼吸道疾病医疗访问的统计预测模型.
主要方法:
- 对医疗访问数据 (2013-2019) 的时间变化分析.
- 应用随机森林模型来识别呼吸系统疾病访问的关键预测因素.
- 使用气象和污染数据构建和验证预测模型.
主要成果:
- 呼吸系统医疗访问显示季节性波动,在2017年显著下降,可能是由于政策变化.
- 平均温度,相对湿度,降水和臭氧度是影响访问的重要因素.
- 随机森林模型在特定范围内 (50-200次访问) 显示了每日变化的强有力的预测性能.
结论:
- 随机森林模型有效地预测了受到特定气象和污染因素影响的每日呼吸道疾病医疗访问.
- 对于中等访问次数,模型准确性很高,但对于极端变化存在限制.
- 未来的研究应该探索混合模型,以提高极端事件的预测准确性.
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