Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster

Abstract

Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it is still challenging to identify LMCI from EMCI because of the subtle changes in imaging which are not noticeable. In addition, there were relatively few studies to make inferences about the brain dynamic changes in the cognitive progression from EMCI to LMCI to AD. Inspired by the above problems, we proposed an advanced approach of evolutionary weighted random support vector machine cluster (EWRSVMC). Where the predictions of numerous weighted SVM classifiers are aggregated for improving the generalization performance. We validated our method in multiple binary classifications using Alzheimer’s Disease Neuroimaging Initiative dataset. As a result, the encouraging accuracy of 90% for EMCI/LMCI and 88.89% for LMCI/AD were achieved respectively, demonstrating the excellent discriminating ability. Furthermore, disease-related brain regions underlying the AD progression could be found out on the basis of the amount of discriminative information. The findings of this study provide considerable insight into the neurophysiological mechanisms in AD development.

Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Blood Oxygenation Level-Dependent Functional Magnetic Resonance Imaging Images in Analysis of Therapeutic Effects on Cerebral Ischemic Stroke

“…Also, blood vessels expand, and local blood flow increases, which will lead to the increase of local oxygen hemoglobin. BOLD-fMRI combined with conventional MRI can clearly observe the location, intensity, and range of excitement in the cerebral cortex caused by exercise [17][18][19].…”

Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning

“…Consequently, each subject has 32 RGB slices which can be seen in Supplementary Material . It is worth noting that some researches have reported that the temporal lobe may make an essential contribution during the early stage of MCI (Bi et al, 2018 ; Cui et al, 2018 ; Zhang et al, 2019 ), and the 32 slices, including the whole temporal lobe and other memory related regions, such as the hippocampus and callosum, were thus selected.…”

Alzheimer's disease detection using depthwise separable convolutional neural networks

“…A study on four-class classification was proposed in [32]: The study investigated the diagnosis of AD, late mild cognitive impairment (LMCI), early mild cognitive impairment (EMCI), and healthy control (HC). Multi-core SVM [33] and the weighted random SVM [34] have also been used for the same types of classification and the performance continuously improved. The detailed recognition accuracy rates from these works are shown in Table I.…”

“…The detailed recognition accuracy rates from these works are shown in Table I. Methods EMCI versus LMCI LMCI versus AD [32] 72.05% 81.70% [33] 73.60% 90.10% [34] 90.00% 88.89%…”