Diagnosis of Alzheimer's disease using universum support vector machine based recursive feature elimination (USVM-RFE)
Diagnosis of Alzheimer's disease using universum support vector machine based recursive feature elimination (USVM-RFE)
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DOI：
10.1016/j.bspc.2020.101903
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发表时间：
2020-05-01
影响因子：
5.1
通讯作者：
Rashid, A. H.
中科院分区：
文献类型：
--
作者：
Richhariya, B.;Tanveer, M.;Rashid, A. H.
阿尔茨海默病是当今世界最常见的死亡原因之一。磁共振成像 (MRI) 为诊断阿尔茨海默病提供了一种有效且非侵入性的方法。 MRI 图像的准确分类需要有效的特征提取技术。受基于支持向量机的递归特征消除（SVM-RFE）[16]工作的推动，我们提出了一种新颖的特征选择技术，将有关数据分布的先验信息纳入递归特征消除过程中。我们的方法被称为基于通用支持向量机的递归特征消除（USVM-RFE）。与 SVM-RFE 中特征选择的局部方法相比，所提出的方法提供了 RFE 过程中数据的全局信息。我们还介绍了特征选择和分类算法在结构 MRI 图像（ADNI 数据库）的基于体素和基于体积的形态测量分析上的应用。使用灰质、白质和脑脊液等脑组织的 MRI 数据进行特征选择。 USVM-RFE 在对照正常 (CN)、轻度认知障碍 (MCI) 和阿尔茨海默病 (AD) 受试者的分类方面比 SVM-RFE 有所改进。此外，与 SVM-RFE 相比，USVM-RFE 通过更少的特征数量获得了更好的精度。这导致识别突出的大脑区域以进行 MRI 图像的特征选择和分类。我们的方法对 CN vs AD、CN vs MCI 和 MCI vs AD 分类的最高准确率分别为 100%、90% 和 73.68%。 (C) 2020 Elsevier Ltd. 保留所有权利。
Alzheimer's disease is one of the most common causes of death in today's world. Magnetic resonance imaging (MRI) provides an efficient and non-invasive approach for diagnosis of Alzheimer's disease. Efficient feature extraction techniques are needed for accurate classification of MRI images. Motivated by the work on support vector machine based recursive feature elimination (SVM-RFE) [16], we propose a novel feature selection technique to incorporate prior information about data distribution in the recursive feature elimination process. Our method is termed as universum support vector machine based recursive feature elimination (USVM-RFE). The proposed method provides global information about data in the RFE process as compared to the local approach of feature selection in SVM-RFE. We also present the application of feature selection and classification algorithms on both voxel based as well as volume based morphometry analysis of structural MRI images (ADNI database). Feature selection is performed using MRI data of brain tissues such as gray matter, white matter, and cerebrospinal fluid. USVM-RFE provides improvement over SVM-RFE in classification of control normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) subjects. Moreover, better accuracy is obtained by USVM-RFE with lesser number of features in comparison to SVM-RFE. This leads to identification of prominent brain regions for feature selection and classification of MRI images. The highest accuracies obtained by our method for classification of CN vs AD, CN vs MCI, and MCI vs AD are 100%, 90%, and 73.68%, respectively. (C) 2020 Elsevier Ltd. All rights reserved.