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首页> 外文期刊>Journal of Theoretical and Applied Information Technology >FEATURE EXTRACTION OF ALZHEIMER?S DISEASE CLASSIFICATION BASED ON PRINCIPAL COMPONENT AND RANDOM SUBSPACE DISCRIMINANT ANALYSIS
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FEATURE EXTRACTION OF ALZHEIMER?S DISEASE CLASSIFICATION BASED ON PRINCIPAL COMPONENT AND RANDOM SUBSPACE DISCRIMINANT ANALYSIS

机译：基于主成分和随机子空间判别分析的阿尔茨米默氏症症分类特征提取

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摘要

Alzheimer?s disease (AD) is one of the diseases which brings great influences on the lives of the people. AD classification can serve as a supportive tool to help the doctor to analyze the brain images. One of the important steps in AD classification is feature extraction. Among the feature extraction techniques, Principal Component Analysis (PCA) is a widely used machine learning approach. Nevertheless, it is hard to decide the number of dimensions to be extracted after the transformation. The accuracy of the classification can be greatly affected by the number of dimensions to be chosen. Therefore, this paper has developed a feature extraction method based on principal component and random subspace discriminant analysis (PCRSDA) to extract and select the features. The selection of the number of dimensions was determined by 10-fold cross validation where the features were selected randomly without replacement. The dataset in this paper was collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) database across four time points. The classification results were 81%, 84%, 87% and 87% at time point of 24 months before stable diagnosis, 18 months before stable diagnosis, 12 months before stable diagnosis and at the stable diagnosis time point, respectively.
机译：阿尔茨海默病（AD）是对人民生活带来巨大影响的疾病之一。广告分类可以作为支持医生分析大脑图像的支持工具。广告分类中的一个重要步骤是特征提取。在特征提取技术中，主成分分析（PCA）是一种广泛使用的机器学习方法。然而，很难确定转换后要提取的尺寸的数量。分类的准确性可能受到要选择的尺寸的数量的大大影响。因此，本文开发了基于主成分和随机子空间判别分析（PCRSDA）的特征提取方法来提取和选择特征。选择尺寸的数量由10倍的交叉验证确定，其中随机选择特征而无需更换。本文的数据集从阿尔茨海默病神经影像倡议（ADNI）数据库中收集了四个时间点。在稳定诊断前24个月的时间点分类结果为81％，84％，87％和87％，稳定诊断前18个月，稳定诊断前12个月，分别在稳定诊断时间点。

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