Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data
Multiclass diagnosis of stages of Alzheimer's disease using linear discriminant analysis scoring for multimodal data
复制标题
使用多模态数据的线性判别分析评分对阿尔茨海默病的各个阶段进行多类诊断
DOI：
10.1016/j.compbiomed.2021.104478
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发表时间：
2021-05-15
影响因子：
7.7
通讯作者：
Tong, Tong
中科院分区：
文献类型：
--
作者：
Lin, Weiming;Gao, Qinquan;Tong, Tong
阿尔茨海默病（AD）是一种进行性神经退行性疾病，轻度认知障碍（MCI）是正常对照（NC）与AD之间的过渡阶段。AD的多类分类是一项困难的任务，因为相邻组之间存在多个相似点。使用多模态数据可以提高分类性能，但由于多模态数据融合效率低，分类性能的提高受到限制。本研究旨在建立一个基于线性判别分析（LDA）评分方法的AD多类别诊断框架，以更有效地融合多模态数据。磁共振成像、正电子发射断层扫描、脑脊液生物标志物和遗传特征首先通过年龄校正、特征选择和特征还原进行预处理。然后分别用LDA评分，得到不同方式下代表AD病理进展的分数。最后，建立了一个基于极限学习机的决策树，利用这些分数进行多类诊断。在AD Neuroimaging Initiative数据集上进行实验，三向和四向分类的准确率分别为66.7%和57.3%，f1评分分别为64.9%和55.7%。结果还表明，该框架比未对多模态数据进行评分的方法和以往研究的方法取得了更好的性能，从而表明LDA评分策略是AD多类分类中多模态融合的有效方法。
Alzheimer's disease (AD) is a progressive neurodegenerative disease, and mild cognitive impairment (MCI) is a transitional stage between normal control (NC) and AD. A multiclass classification of AD is a difficult task because there are multiple similarities between neighboring groups. The performance of classification can be improved by using multimodal data, but the improvement could be limited with inefficient fusion of multimodal data. This study aims to develop a framework for AD multiclass diagnosis with a linear discriminant analysis (LDA) scoring method to fuse multimodal data more efficiently. Magnetic resonance imaging, positron emission tomography, cerebrospinal fluid biomarkers, and genetic features were first preprocessed by performing age correction, feature selection, and feature reduction. Then, they were individually scored using LDA, and the scores that represent the AD pathological progress in different modalities were obtained. Finally, an extreme learning machine-based decision tree was established to perform multiclass diagnosis using these scores. The experiments were conducted on the AD Neuroimaging Initiative dataset, and accuracies of 66.7% and 57.3% and F1-scores of 64.9% and 55.7% were achieved in three- and four-way classifications, respectively. The results also showed that the proposed framework achieved a better performance than the method that did not score multimodal data and the methods in previous studies, thereby indicating that the LDA scoring strategy is an efficient way for multimodalities fusion in AD multiclass classification.