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An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment
Yao, Dongren1,2,3; Calhoun, Vince D.5,6,7; Fu, Zening5; Du, Yuhui5,8; Sui, Jing1,2,3,4
AbstractDiscriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. (C) 2018 Elsevier B.V. All rights reserved.
KeywordMulti-class Classification Feature Selection Alzheimer's Disease(Ad) Mild Cognitive Impairment (Mci) Structural Mri Hierarchical Classification Relative Importance
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Funding OrganizationNational High Tech Development Program (863 Plan)(2015AA020513) ; China National Natural Science Foundation(81471367 ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060005) ; National Institute of Health(1R01EB00584 ; 61773380) ; 1R01MH094524 ; P20GM103472)
WOS Research AreaBiochemistry & Molecular Biology ; Neurosciences & Neurology
WOS SubjectBiochemical Research Methods ; Neurosciences
WOS IDWOS:000432615900011
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China
2.Chinese Acad Sci, NLPR, Inst Automat, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Inst Automat, CAS Ctr Excellence Brain Sci, Beijing, Peoples R China
5.Mind Res Network, Albuquerque, NM USA
6.Univ New Mexico, Dept Psychiat & Neurosci, Albuquerque, NM 87131 USA
7.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
8.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Shanxi, Peoples R China
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GB/T 7714
Yao, Dongren,Calhoun, Vince D.,Fu, Zening,et al. An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment[J]. JOURNAL OF NEUROSCIENCE METHODS,2018,302:75-81.
APA Yao, Dongren,Calhoun, Vince D.,Fu, Zening,Du, Yuhui,&Sui, Jing.(2018).An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.JOURNAL OF NEUROSCIENCE METHODS,302,75-81.
MLA Yao, Dongren,et al."An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment".JOURNAL OF NEUROSCIENCE METHODS 302(2018):75-81.
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