A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer's Disease Study.
A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer's Disease Study.
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一种基于极限学习机的阿尔茨海默病研究关键特征筛选新方法
DOI：
10.3389/fnagi.2022.888575
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发表时间：
2022
影响因子：
4.8
通讯作者：
中科院分区：
文献类型：
--
作者：
极限学习机（ELM）是一种简单高效的单隐层前馈神经网络（SLFN）算法。近年来，它逐渐被用于阿尔茨海默病（AD）的研究。在利用ELM基于高维特征诊断AD时，往往有一些特征对诊断没有积极影响，而另一些特征对诊断有显著影响。提出了一种基于极限学习机（KFS-ELM）的关键特征筛选方法。它可以筛选与分类（诊断）相关的关键特征。它还可以根据关键特征的重要性分配权重。我们设计了一个实验来筛选AD的关键特征。从4005个功能连接中筛选出920个关键功能连接。同时也得到了它们的重量。实验结果表明：(1)利用全部（4,005）个特征诊断AD，准确率为95.33%。利用920个关键特征诊断AD，准确率达99.20%。筛选出的3085（4005 - 920）个特征对阿尔茨海默病的诊断有负面影响。这表明KFS-ELM在筛选关键特征方面是有效的。(2)关键特征权重越大、数量越少，对AD诊断的影响越大。这表明KFS-ELM在为关键特征的重要性分配权重方面是合理的。因此，KFS-ELM既可以作为研究特征的工具，也可以作为提高分类精度的工具。
The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer’s disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy.
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