人工智能
支持向量机
随机森林
旋回作用
线性判别分析
计算机科学
模式识别(心理学)
二元分类
稳健性(进化)
逻辑回归
决策树
机器学习
心理学
神经科学
生物
大脑皮层
基因
生物化学
作者
Zengbei Yuan,Xufeng Yao,Xixi Bu
标识
DOI:10.1109/icpeca53709.2022.9718827
摘要
The conventional machine learning (ML) methods have been used in the classification of Alzheimer’s disease (AD), while the performance was still needed to be addressed for being short of the standard datasets and implemented approaches. In our study, one scheme was proposed to verify the robustness of proposed methods. A total of one hundred T1-MRI data were downloaded and divided into three groups: AD (34 cases), mind cognition impairment (MCI) (45 cases) and normal control (NC) (21 cases). In our method, four cortical characteristics of cortical thickness (CT), gray matter volume (GMV), local gyrification index (LGI) and cortical surface area (CSA) for brain regions and one genetic characteristic of ApoE alleles were used. With the optimized characteristics by one-way analysis of variance (ANOVA), Krystal-Wallis H test and ReliefF algorithms, five MLs including random forest (RF), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR) and decision trees (DTs) are compared. With the improved cortical features, the classifiers of RF and LDA showed the highest accuracy of 89% for the binary classification (AD vs NC). The DTs and SVM classifier achieved 92% for the three-classification (AD vs MCI vs NC). The chosen brain cortical and genetic characteristics showed valuable capability in AD classification and the proposed scheme was robust for performance evaluation.
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