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基于性别的数据偏差和模型公平性评估在基准的开放访问疾病预测数据集中的基准数据

Shahadat Uddin1, Huan Liang2, Haolan Guo2

  • 1School of Electrical and Computer Engineering, Software Engineering Group, The University of Sydney, Darlington, NSW, Australia.

Computers in biology and medicine
|January 28, 2026
PubMed
概括

机器学习 (ML) 数据集中的性别偏见不成比例地影响女性,特别是在心脏病预测方面. 解决这种数据偏差和选择适当的算法,如决策树,对于医疗保健中的公平人工智能至关重要.

关键词:
数据偏差是一种数据偏差.疾病预测 疾病预测性别 性别 性别 性别模型的公平性.开放式访问数据集

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