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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Application of attention network test and demographic information to detect mild cognitive impairment via combining feature selection with support vector machine.
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Application of attention network test and demographic information to detect mild cognitive impairment via combining feature selection with support vector machine.

机译：注意网络测试和人口统计信息通过将特征选择与支持向量机相结合来检测轻度认知障碍的应用。

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摘要

Mild cognitive impairment (MCI) is now thought as the prodromal phase of Alzheimer's disease (AD), and the usual method for diagnosing the disease would be a battery of neuropsychological assessment. The present study proposes to integrate a feature selection scheme with support vector machine (SVM) to identify patients with MCI by using attention network test (ANT) and demographic data. Forty-two patients with MCI and forty-five normal individuals underwent ANT recording, and the reaction time and accuracy of ANT and demographics (age, gender, and educational level) were selected as original features. To select features, we first introduced some random variables as probe features in the original data, then ranked all the features according to their influence on the support vector machine decision function, and finally selected those features that had an influence higher than that of the probes. Initially 18 different features were reduced to only four features by our method. SVM classifier created by using these four features gave an 85% classification accuracy with a sensitivity of 85% and a specificity of 86%. And the area under the curve obtained by receiver operating characteristics analysis was 0.918. The experimental results demonstrate that the proposed method is a good potential use to assist identifying patients with MCI objectively and efficiently.
机译：轻度认知障碍（MCI）现在被认为是阿尔茨海默氏病（AD）的前驱期，诊断该病的常用方法是进行一系列神经心理学评估。本研究提出将特征选择方案与支持向量机（SVM）集成，以通过使用注意力网络测试（ANT）和人口统计数据来识别MCI患者。 42例MCI患者和45例正常人进行了ANT记录，并且选择ANT的反应时间和准确性以及人口统计学（年龄，性别和教育程度）作为原始特征。为了选择特征，我们首先在原始数据中引入一些随机变量作为探针特征，然后根据它们对支持向量机决策函数的影响对所有特征进行排名，最后选择影响力高于探针的那些特征。最初，我们的方法将18个不同的特征缩减为仅四个特征。使用这四个功能创建的SVM分类器可实现85％的分类精度，灵敏度为85％，特异性为86％。通过接收器工作特性分析获得的曲线下面积为0.918。实验结果表明，该方法在协助客观有效地识别MCI患者方面具有良好的应用前景。

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