Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran.
Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran.
Comput Intell Neurosci. 2022 Aug 9;2022:7413081. doi: 10.1155/2022/7413081. eCollection 2022.
There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of -nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy.
阿尔茨海默病（AD）是一种神经退行性疾病，会导致认知能力下降、日常生活恶化以及行为和心理变化，其影响多种多样。载脂蛋白 E 基因 4 的多态性被认为是阿尔茨海默病的遗传风险因素。本文旨在证明单核苷酸多态性标记（SNP）与定量正电子发射断层扫描（PET）成像特征之间存在因果关系。此外，还使用最近邻（KNN）、支持向量机（SVM）、线性判别分析（LDA）和卷积神经网络（CNN）技术的组合，根据 PET 图像中脑组织变化的频率对 AD 进行分类。结果表明，与载脂蛋白 E 基因中的 SNP 相比，所提出的 SNP 似乎与定量特征的相关性更强。在分类结果方面，CNN 的准确率最高，为 91.1%。这些结果表明 KNN 和 CNN 方法有助于 AD 的诊断。然而，LDA 和 SVM 的准确率较低。
