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J Neurosci Methods. 2018 May 15;302:75-81. doi: 10.1016/j.jneumeth.2018.03.008. Epub 2018 Mar 22.

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

Author information

1
Brainnetome Center and NLPR, Institute of Automation, CAS, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
2
The Mind Research Network, NM, USA; Dept. of Psychiatry and Neuroscience, University of New Mexico, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, NM, USA.
3
The Mind Research Network, NM, USA.
4
The Mind Research Network, NM, USA; Shanxi University, School of Computer & Information Technology, Taiyuan, China.
5
Brainnetome Center and NLPR, Institute of Automation, CAS, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science, Institute of Automation, Beijing, China. Electronic address: kittysj@gmail.com.

Abstract

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.

KEYWORDS:

Alzheimer’s disease(AD); Feature selection; Hierarchical classification; Mild cognitive impairment (MCI); Multi-class classification; Relative importance; Structural MRI

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