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Basics of Multivariate Analysis in Neuroimaging Data
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通过交叉验证比较基于神经成像的分类模型的准确性的统计变异性.
Bahram Jafrasteh1, Ehsan Adeli2,3, Kilian M Pohl2
1Department of Radiology, Weill Cornell Medicine, New York, NY, USA. baj4003@med.cornell.edu.
Scientific reports
|August 6, 2025
概括
在生物医学研究中比较机器学习 (ML) 模型准确性是具有挑战性的,特别是交叉验证 (CV). 本研究提出了一个框架来评估CV对统计学意义的影响,突出了变化和需要严格的比较实践的需要.
科学领域:
- 生物医学研究的研究.
- 机器学习应用程序 机器学习应用程序
- 神经成像分析分析神经成像分析
背景情况:
- 机器学习 (ML) 在生物医学研究中提高了分类准确性.
- 严格比较ML模型准确性是必不可少的,但具有挑战性.
- 交叉验证 (CV) 引入了对ML模型的统计显著性测试的复杂性.
研究的目的:
- 突出使用CV的ML模型之间的精度差异的统计学意义量化的实际挑战.
- 提出一个公正的框架来评估CV设置对统计学意义的影响.
- 解决生物医学ML研究中更严格实践的需要,以减轻可重现性问题.
主要方法:
- 开发了一个公正的框架来评估CV配置对统计学意义的影响.
- 将框架应用于三个公共的神经成像数据集.
- 分析了数据属性,测试程序和CV选择对检测显著差异的影响.
主要成果:
- 在检测基于数据属性,测试程序和CV配置的ML模型显著差异方面表现出实质性的可变性.
- 重新强调了当前p值计算中的已知缺陷,用于比较模型准确性.
- 表明影响重要性的因素在基于ML的生物医学研究中经常被忽视.
结论:
- 显著性测试中的变量可以导致p-hacking和对模型改进的不一致结论.
- 迫切需要更严格的实践来比较生物医学研究中的ML模型.
- 实施可靠的比较方法对于解决现场可重现性危机至关重要.
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