Random support vector machine cluster analysis of resting-state fMRI in Alzheimer's disease

被引:42
作者
Bi, Xia-an [1 ]
Shu, Qing [1 ]
Sun, Qi [1 ]
Xu, Qian [1 ]
机构
[1] Hunan Normal Univ, Coll Informat Sci & Engn, Changsha, Hunan, Peoples R China
来源
PLOS ONE | 2018年 / 13卷 / 03期
基金
美国国家科学基金会;
关键词
MILD COGNITIVE IMPAIRMENT; FUNCTIONAL CONNECTIVITY; ACTION RESTRAINT; FEATURE-RANKING; STRUCTURAL MRI; CORTEX LESIONS; BRAIN; CLASSIFICATION; METAANALYSIS; PREDICTION;
D O I
10.1371/journal.pone.0194479
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:17
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