机器学习归算方法的多度比较与乳腺癌存活率的应用
Imad El Badisy1,2,3, Nathalie Graffeo4, Mohamed Khalis5,6
1Mohammed VI Center For Research and Innovation, Rabat, Morocco. ielbadisy@um6ss.ma.
BMC medical research methodology
|August 30, 2024
概括
机器学习归算方法改善了临床预后研究. 像miceCART和miceRF这样的多重归算 (MI) 方法可以减少偏差,而单一归算 (SI) 方法提供更好的预测准确性,但增加偏差.
科学领域:
- 临床流行病学 临床流行病学
- 生物统计学 生物统计学
- 机器学习应用 机器学习应用
背景情况:
- 在临床预后研究中,处理缺失的数据至关重要.
- 机器学习 (ML) 归算方法提供了潜在的解决方案,但需要进行彻底的评估.
- 现有方法的有效性因分析目标和数据特征而异.
研究的目的:
- 综合评估各种ML归算方法的有效性和可靠性.
- 为了比较单次归算 (SI) 和多次归算 (MI) 技术.
- 评估不同性能指标的方法,包括偏差,预测准确度和无模型指标.
主要方法:
- 评估了单次归算 (KNN,missMDA,CART,missForest,missRanger,missCforest) 和多次归算 (miceCART,miceRF) 的方法.
- 利用一个模拟数据集,其中有30%的随机失踪 (MAR) 值和一个真实世界乳腺癌存活率研究.
- 使用Gower距离,估计偏差,预测准确度 (AUC,C指数) 和信心区间覆盖率等指标评估性能.
主要成果:
- 多重归算 (MI) 方法 (miceCART,miceRF) 在回归估计中显示出最少的偏差.
- 单一归算 (SI) 方法 (missMDA,missForest) 证明了Cox模型的更高的预测准确性.
- MI方法提供了最佳的置信区间覆盖,优于完整病例分析 (CCA).
结论:
- 机器学习归算方法通常优于完整案例分析.
- 归算方法的选择 (SI与MI) 取决于优先考虑的是最小化偏差还是最大化预测准确性.
- 在多重归算框架中集成先进的ML算法可以提高时间到事件研究中的研究完整性.
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