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Citation
Please cite as:
liu q, ma c, liu m, Chen S, yu m, xia l, 张 q, Wu M
Reducing Educational Bias in Cognitive Assessment via Dynamic Support Vector Machine Weighting: Validation Study on an Education-Stratified Dataset
JMIR Rehabil Assist Technol 2026;13:e79841
DOI: 10.2196/79841
PMID: 41740092
PMCID: 12935291
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Date Submitted: Jun 30, 2025
Date Accepted: Dec 23, 2025
Date Submitted to PubMed: Jan 8, 2026
Educing Educational Bias in Cognitive Assessment via Dynamic SVM Weighting: Validation Study on an Education-Stratified Dataset
qing liu;
chi ma;
mengyuan liu;
Suhui Chen;
mengting yu;
lijuan xia;
qi 张;
Ming Wu
ABSTRACT
Background:
针对不同学历水平的认知障碍或者潜在认知障碍人群制定量身的认知筛查量表
Objective:
调查教育背景对认知评估迷你精神状态考试(MMSE)预测效果的影响,揭示跨教育层认知子任务的差异贡献,
Methods:
812名参与者的数据根据教育背景分为四组:文盲、小学、中学和大学。首先进行了系统的子项目删除实验,以评估每个教育组内每个MMSE认知子项目的歧视性有效性(分为正增益、负干扰或中性效应)和贡献水平(量化为预测率的Δ变化)。随后,使用支持向量机(SVM)构建了一个教育分层动态加权模型。
Results:
文盲组的认知评估显示对空间取向的高度依赖,没有发现显著的负面干扰因素。小学组同样依赖于空间取向,但时间取向成为一个基本的积极指标;同时,该组经历了“空间结构”子项的干扰。中学组表现出过渡性认知评估模式,其特点是对时间取向和计算能力的依赖增加,但面临来自多个子项的负面预测影响。大学小组确立了执行功能和计算能力作为核心决定因素,同时受到基本任务和语言任务的干扰。在基于SVM的动态加权分配子项目后,所有教育群体的预测率都提高了。文盲组(Δ=7.25%)有最大的进步,其次是小学组(Δ=3.12%)。
Conclusions:
本研究使用基于子项贡献分析的动态加权SVM模型优化了MMSE量表,显著提高了不同教育背景的认知评估效率——特别是对文盲和小学队列等低教育群体。结果表明,教育背景对MMSE的预测效果有重大影响,不同教育水平的群体之间认知评估概况存在显著差异。这项研究为跨教育群体的个性化认知评估提供了数据驱动的支持,为临床实践和认知理论发展提供了宝贵的见解。 Clinical Trial: 2024-RE-431
Citation
Please cite as:
liu q, ma c, liu m, Chen S, yu m, xia l, 张 q, Wu M
Reducing Educational Bias in Cognitive Assessment via Dynamic Support Vector Machine Weighting: Validation Study on an Education-Stratified Dataset
JMIR Rehabil Assist Technol 2026;13:e79841
DOI: 10.2196/79841
PMID: 41740092
PMCID: 12935291
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