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首页> 外文期刊>Journal of Neuroscience Methods >An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment
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An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment

机译：用于Alzheimer疾病和轻度认知障碍的4路分类的集合学习系统

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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. (C) 2018 Elsevier B.V. All rights reserved.
机译：鉴别阿尔茨海默病（AD）从其前素形式，轻度认知障碍（MCI）是一个重要的临床问题，可以促进早期诊断和干预，其中一个更具挑战性的问题是分类MCI亚型，即最终转型的人广告（CMCI）与那些没有（MCI）的人。为解决这一困难的4路分类问题（广告，MCI，CMCI和健康控制），演戏表由演播者举办，邀请科学界将其机器学习方法应用于预处理的T1加权磁共振图像（ MRI）数据和国际阿尔茨海默病神经影像倡议（ADNI）数据库的人口统计信息。本文总结了我们的竞争结果。我们首先通过将4路分类转化为五个二进制分类问题来提出分层过程。还提出了一种基于相对重要性的新特征选择技术，旨在识别426 SMRI形态学和3个人口统计特征的更具信息丰富和简洁的子集，以确保每个二进制分类器实现其最高精度。因此，选择了大约2％的原始功能来构建一个新的特征空间，可以通过分层分组测试数据的最终四向分类，比较高于几种替代方法。更重要的是，诸如海马体积，总比波处理区和内侧轨道厚度等的所选择的辨别特征以及MMSE评分是合理的，并且与AD / MCI缺陷报告的那些相一致。总之，所提出的方法使用分层分组和精确特征选择提供了一种用于多路分类的新框架。 （c）2018 Elsevier B.v.保留所有权利。

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