College of Information Science and Engineering, Hunan Normal University, Changsha, P.R. China.
PLoS One. 2018 Mar 23;13(3):e0194479. doi: 10.1371/journal.pone.0194479. eCollection 2018.
Early diagnosis is critical for individuals with Alzheimer's disease (AD) in clinical practice because its progress is irreversible. In the existing literature, support vector machine (SVM) has always been applied to distinguish between AD and healthy controls (HC) based on neuroimaging data. But previous studies have only used a single SVM to classify AD and HC, and the accuracy is not very high and generally less than 90%. The method of random support vector machine cluster was proposed to classify AD and HC in this paper. From the Alzheimer's Disease Neuroimaging Initiative database, the subjects including 25 AD individuals and 35 HC individuals were obtained. The classification accuracy could reach to 94.44% in the results. Furthermore, the method could also be used for feature selection and the accuracy could be maintained at the level of 94.44%. In addition, we could also find out abnormal brain regions (inferior frontal gyrus, superior frontal gyrus, precentral gyrus and cingulate cortex). It is worth noting that the proposed random support vector machine cluster could be a new insight to help the diagnosis of AD.
早期诊断对于临床实践中的阿尔茨海默病（AD）患者至关重要，因为其病情进展是不可逆的。在现有文献中，支持向量机（SVM）一直被应用于基于神经影像学数据区分 AD 和健康对照者（HC）。但之前的研究仅使用单一 SVM 对 AD 和 HC 进行分类，准确性不是很高，通常低于 90%。本文提出了随机支持向量机聚类的方法来对 AD 和 HC 进行分类。从阿尔茨海默病神经影像学倡议数据库中获取了包括 25 名 AD 个体和 35 名 HC 个体的受试者。结果显示分类准确率可达 94.44%。此外，该方法还可用于特征选择，准确率可保持在 94.44%的水平。另外，我们还可以发现异常的脑区（额下回、额上回、中央前回和扣带回）。值得注意的是，所提出的随机支持向量机聚类方法可能为 AD 的诊断提供新的思路。
