在健康和退休研究中,调节回归优于树来预测认知功能.
Kyle Masato Ishikawa1, Deborah Taira2, Joseph Keaweʻaimoku Kaholokula3
1Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo St, Honolulu, HI, USA.
概括
与基于树的机器学习模型相比,弹性净回归模型在检测认知衰退方面表现优越. 基线认知功能和计算机使用频率是关键预测因素,突出了线性关系对认知结果建模的重要性.
科学领域:
- 机器学习在医疗保健中的应用
- 认知衰退研究研究认知衰退研究
- 生物统计学 生物统计学
背景情况:
- 通用线性模型 (GLMs) 在医疗保健中受欢迎,因为它具有可解释性.
- 基于树木的模型 (例如随机森林,增强树木) 在预测性能方面表现出色,但缺乏透明度.
- 临床应用需要可解释的模型来了解患者和可操作的见解.
研究的目的:
- 使用机器学习 (ML) 来检测认知衰退.
- 为了能够及时查认知障碍.
- 发现认知衰退和心理社会决定因素之间的关联.
主要方法:
- 来自2018-2020年健康和退休研究的雇员数据.
- 开发了三种线性回归和三种基于树的模型.
- 使用RMSE和R-squared评估模型性能,并通过系数,变量重要性和决策树解释性.
主要成果:
- 弹性净回归实现了最好的表现 (RMSE = 3.520,R2 = 0.435).
- 基线认知功能和计算机使用频率成为所有模型中最重要的预测因素.
- 线性模型,特别是弹性网,在这个数据集中显示出比基于树的方法更好的性能.
结论:
- 弹性净回归在预测认知结果方面超过了基于树的模型.
- 增量线性关系似乎是模拟认知衰退的最佳模式.
- 弹性网的特征选择平衡了认知健康数据的解释性和预测能力.
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