Knowledge Commons of Institute of Automation,CAS
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 | |
发表期刊 | Journal of Neuroscience Methods |
2018 | |
期号 | ****页码:*** |
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. |
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 | 脑图谱与类脑智能实验室_脑网络组研究 |
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):***. |
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