动态模式分解与其他数据驱动模型的比较,用于预测肺癌发病率
L Raymond Guo1, Jifu Tan2, M Courtney Hughes3
1Department of Interdisciplinary Sciences, Northern Illinois University, DeKalb, IL, United States.
Frontiers in public health
|May 12, 2025
概括
动态模式分解 (DMD) 为分析公共卫生数据提供了一种高效的方法,与传统机器学习模型相比,它对理解肺癌趋势具有较低的计算需求.
科学领域:
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
- 计算流行病学计算流行病学
背景情况:
- 分析公共卫生数据,特别是与时空因素相关的数据,对传统方法提出了挑战.
- 机器学习模型提供先进的分析能力,但可能资源密集,需要专门的专业知识.
- 动态模式分解 (DMD) 为分析复杂数据集提供了一个计算效率高的替代方案.
研究的目的:
- 评估动态模式分解 (DMD) 在公共卫生数据分析中的应用.
- 将DMD的性能与各种机器学习模型进行比较,以分析肺癌发病率.
- 评估DMD对识别公共卫生趋势的有用性.
主要方法:
- 分析了2000年至2021年间1,013个美国县的肺癌发病率数据.
- 使用了标准的机器学习模型 (随机森林,梯度增强机,支向量机),时间序列,线性回归和DMD.
- 模型的性能由其在预测2021年肺癌发病率方面的准确性来评估.
主要成果:
- 时间序列模型产生了最小的根平均平方误差,其次是随机森林模型.
- 动态模式分解 (DMD) 显示了与随机森林模型相比的根平均平方误差.
- 地理分析显示,肯塔基州的肺癌发病率较高,加利福尼亚州,新墨西哥州,犹他州和爱达荷州的发病率较低.
结论:
- 动态模式分解 (DMD) 是用于公共卫生数据分析的可行和高效替代方案.
- DMD可以有效地捕捉公共卫生数据的潜在趋势,可能减少计算需求.
- 这些发现表明,DMD可以帮助公共卫生专业人员了解疾病模式.
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