干旱区生态环境知识资源中心(Arid): Alzheimer's Disease Diagnosis Based on Cortical and Subcortical Features

Arid

DOI	10.1155/2019/2492719
	Alzheimer's Disease Diagnosis Based on Cortical and Subcortical Features
	Gupta, Yubraj 1,2; Lee, Kun Ho 2,3; Choi, Kyu Yeong 2; Lee, Jang Jae 2; Kim, Byeong Chae 2,4; Kwon, Goo-Rak 1,2
通讯作者	Kwon, Goo-Rak
来源期刊	JOURNAL OF HEALTHCARE ENGINEERING
ISSN	2040-2295
EISSN	2040-2309
出版年	2019
卷号	2019
英文摘要	Alzheimer's disease (AD) is a common neurodegenerative disease with an often seen prodromal mild cognitive impairment (MCI) phase, where memory loss is the main complaint progressively worsening with behavior issues and poor self-care. However, not all patients clinically diagnosed with MCI progress to the AD. Currently, several high-dimensional classification techniques have been developed to automatically distinguish among AD, MCI, and healthy control (HC) patients based on T1-weighted MRI. However, these method features are based on wavelets, contourlets, gray-level co-occurrence matrix, etc., rather than using clinical features which helps doctors to understand the pathological mechanism of the AD. In this study, a new approach is proposed using cortical thickness and subcortical volume for distinguishing binary and tertiary classification of the National Research Center for Dementia dataset (NRCD), which consists of 326 subjects. Five classification experiments are performed: binary classification, i.e., AD vs HC, HC vs mAD (MCI due to the AD), and mAD vs aAD (asymptomatic AD), and tertiary classification, i.e., AD vs HC vs mAD and AD vs HC vs aAD using cortical and subcortical features. Datasets were divided in a 70/30 ratio, and later, 70% were used for training and the remaining 30% were used to get an unbiased estimation performance of the suggested methods. For dimensionality reduction purpose, principal component analysis (PCA) was used. After that, the output of PCA was passed to various types of classifiers, namely, softmax, support vector machine (SVM), k-nearest neighbors, and naive Bayes (NB) to check the performance of the model. Experiments on the NRCD dataset demonstrated that the softmax classifier is best suited for the AD vs HC classification with an F1 score of 99.06, whereas for other groups, the SVM classifier is best suited for the HC vs mAD, mAD vs aAD, and AD vs HC vs mAD classifications with the F1 scores being 99.51, 97.5, and 99.99, respectively. In addition, for the AD vs HC vs aAD classification, NB performed well with an F1 score of 95.88. In addition, to check our proposed model efficiency, we have also used the OASIS dataset for comparing with 9 state-of-the-art methods.
类型	Article
语种	英语
国家	South Korea
开放获取类型	Green Published, Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000461671600001
WOS关键词	MILD COGNITIVE IMPAIRMENT ; DEMENTIA ; PATTERNS ; MRI
WOS类目	Health Care Sciences & Services
WOS研究方向	Health Care Sciences & Services
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/217103
作者单位	1.Chosun Univ, Sch Informat Commun Engn, 309 Pilmun Daero, Gwangju 61452, South Korea; 2.Chosun Univ, Natl Res Ctr Dementia, 309 Pilmun Daero, Gwangju 61452, South Korea; 3.Chosun Univ, Coll Nat Sci, Dept Biomed Sci, 309 Pilmun Daero, Gwangju 61452, South Korea; 4.Chonnam Natl Univ, Sch Med, Dept Neurol, Gwangju 61469, South Korea
推荐引用方式 GB/T 7714	Gupta, Yubraj,Lee, Kun Ho,Choi, Kyu Yeong,et al. Alzheimer's Disease Diagnosis Based on Cortical and Subcortical Features[J],2019,2019.
APA	Gupta, Yubraj,Lee, Kun Ho,Choi, Kyu Yeong,Lee, Jang Jae,Kim, Byeong Chae,&Kwon, Goo-Rak.(2019).Alzheimer's Disease Diagnosis Based on Cortical and Subcortical Features.JOURNAL OF HEALTHCARE ENGINEERING,2019.
MLA	Gupta, Yubraj,et al."Alzheimer's Disease Diagnosis Based on Cortical and Subcortical Features".JOURNAL OF HEALTHCARE ENGINEERING 2019(2019).