Alzheimer’s disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.

中文翻译：

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机器学习的障碍和阿尔茨海默氏病的多模式诊断分类

阿尔茨海默氏病（AD）是最常见的痴呆类型。疾病的确切原因和治疗方法仍不清楚。诸如磁共振成像（MRI），正电子发射断层扫描和单光子发射计算机断层扫描等不同的神经成像方式在AD的研究中发挥了重要作用。但是，AD的有效诊断以及轻度认知障碍（MCI）最近引起了广泛关注。机器人，全球定位系统技术，传感器和机器学习（ML）算法等各种技术进步已经帮助改善了AD的诊断过程。这项研究旨在确定在MRI中实施不同ML分类器的影响，并分析支持向量机与各种多模式扫描对AD / MCI患者和健康对照者的分类情况。根据采用不同的分类器技术并提出用于AD分类的最佳多峰范式已得出结论。