基于免疫的机器学习 预测精神分裂症和双相情感障碍的诊断和疾病状态:如何避免数据偏差和过度匹配
Katrien Skorobogatov1, Pieter Meysman2, Manuel Morrens1
1Scientific Initiative for Neuropsychiatric and Psychopharmacological Studies (SINAPS), University Psychiatric Hospital Campus Duffel (UPCD), Rooienberg 19, 2570 Duffel, Belgium; Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Campus Drie Eiken, S.003, Universiteitsplein 1, 2610 Wilrijk, Belgium.
Brain, behavior, and immunity
|December 19, 2024
概括
本研究通过详细介绍用于防止偏见和确保准确性的严格方法来解决机器学习 (ML) 的批评. 我们的发现证实了对ML分析和验证的最佳实践的遵守.
科学领域:
- 计算生物学是一种计算生物学.
- 数据科学是数据科学.
背景情况:
- 最近的一篇评论引起了人们对机器学习 (ML) 陷的普遍担忧.
- 这些担忧虽然在ML中是有效的,但并没有专门解决我们研究的方法或发现.
研究的目的:
- 通过重申我们ML方法的稳定性来回应批评.
- 为了证明潜在的ML风险在我们的研究中被主动管理.
主要方法:
- 使用了严格的ML技术,包括嵌套交叉验证和分层采样.
- 绩效指标被全面评估,以减轻过度装配和类不平衡.
- 传统的统计方法 (ANCOVA,斯皮尔曼相关性) 补充了ML分析以验证.
主要成果:
- 我们的分析成功地减轻了常见的ML风险,如过度装配,类不平衡和偏差.
- 使用传统统计方法的验证证实了ML的发现.
- 批评中没有发现任何具体的方法缺陷.
结论:
- 该研究的ML分析遵循最佳实践和强大的验证协议.
- 充分解决了ML的潜在陷,确保了我们的研究结果的可靠性.
- 研究方法是健全的,符合已确立的科学标准.
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