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用机器学习模型对阿尔茨海默病诊断中的种族偏见进行系统的公平评估

Neha Goud Baddam^1,2,3, Bizhan Alipour Pijani^1,2,3, Serdar Bozdag^1,2,3,4

¹Department of Computer Science and Engineering, University of North Texas, Denton, TX.

bioRxiv : the preprint server for biology

|November 19, 2025

概括

对于阿尔茨海默病 (AD) 诊断的机器学习 (ML) 模型在对有限的种族群体进行训练时会显示偏差. 应用公平技术可以改善不同人群的模型性能.

科学领域:

神经学神经学
人工智能的人工智能
生物医学信息学生物医学信息学

背景情况:

阿尔茨海默病 (AD) 构成了全球重大健康挑战,预测显示病例将大幅增加.
机器学习 (ML) 模型越来越多地用于AD诊断和进展预测.
现有的ML模型可能会由于训练数据集中缺乏种族多样性而表现出偏见,这会影响它们的概括性.

研究的目的:

在不同种族群体的AD诊断中调查ML模型的公平性.
为了测试这一假设,在一个种族群体上训练的ML模型在其他群体上表现不佳.
评估公平技术在缓解ADML模型中的偏差方面的有效性.

主要方法:

雇员特征选择和模型培训技术,以提高公平性.
在多种种族人口统计学中调查了ML模型的性能.
应用公平干预来减少算法偏差.

主要成果:

证实,在一个种族群体上训练的ML模型在其他种族群体上表现不佳.
证明,实施公平性技术显著减少了对AD的ML模型中的偏差.
突出了与数据集人口统计相关的绩效差异.

结论:

在用于训练AD预测模型的数据集中强调了种族多样性的关键需求.
强调开发和部署公平的ML模型的重要性,以确保公平的医疗保健结果.
倡导继续研究医学AI中偏见缓解策略.

关键词:

算法偏见是一种算法偏见.阿尔茨海默氏症是阿尔茨海默氏症的疾病.公平的公平的公平的机器学习机器学习

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