CASIA OpenIR  > 脑网络组研究中心
An Ensemble Learning System for a 4-Way Classification of Alzheimer’s Disease and Mild Cognitive Impairment
Yao, Dongren1; Vince, D. Calhoun2; Fu, Zening2; Du, Yuhui2; Sui, Jing1
2018
发表期刊Journal of Neuroscience Methods
期号****页码:***
其他摘要Discriminating 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.
关键词Multi-class Classification Feature Selection Alzheimer’s Disease(Ad) Mild Cognitive Impairment (Mci) Structural Mri Hierarchical Classification Relative Importance
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/20863
专题脑网络组研究中心
作者单位1.中国科学院自动化研究所
2.the Mind Research Network
第一作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
Yao, Dongren,Vince, D. Calhoun,Fu, Zening,等. An Ensemble Learning System for a 4-Way Classification of Alzheimer’s Disease and Mild Cognitive Impairment[J]. Journal of Neuroscience Methods,2018(****):***.
APA Yao, Dongren,Vince, D. Calhoun,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(****),***.
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 .****(2018):***.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Yao, Dongren]的文章
[Vince, D. Calhoun]的文章
[Fu, Zening]的文章
百度学术
百度学术中相似的文章
[Yao, Dongren]的文章
[Vince, D. Calhoun]的文章
[Fu, Zening]的文章
必应学术
必应学术中相似的文章
[Yao, Dongren]的文章
[Vince, D. Calhoun]的文章
[Fu, Zening]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。