We analyzed a data set containing functional brain images from 6 healthy controls and 196 individuals with Parkinson's disease (PD), who were divided into five stages according to illness severity. The goal was to predict patients' PD illness stages by using their functional brain images. We employed the following prediction approaches: multivariate statistical methods (linear discriminant analysis, support vector machine, decision tree, and multilayer perceptron [MLP]), ensemble learning models (random forest [RF] and adaptive boosting), and deep convolutional neural network (CNN). For statistical and ensemble models, various feature extraction approaches (principal component analysis [PCA], multilinear PCA, intensity summary statistics [IStat], and Laws' texture energy measure) were employed to extract features, the synthetic minority over‐sampling technique was used to address imbalanced data, and the optimal combination of hyperparameters was found using a grid search. For CNN modeling, we applied an image augmentation technique to increase and balance data sizes over different disease stages. We adopted transfer learning to incorporate pretrained VGG16 weights and architecture into the model fitting, and we also tested a state‐of‐the‐art machine learning model that could automatically generate an optimal neural architecture. We found that IStat consistently outperformed other feature extraction approaches. MLP and RF were the analytic approaches with the highest prediction accuracy rate for multivariate statistical and ensemble learning models, respectively. Overall, the deep CNN model with pretrained VGG16 weights and architecture outperformed other approaches; it captured critical features from imaging, effectively distinguished between normal controls and patients with PD, and achieved the highest classification accuracy.

中文翻译：

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功能脑图像的多类机器学习分类，用于帕金森氏病的阶段预测

我们分析了一个数据集，其中包含来自6名健康对照和196名帕金森氏病（PD）个体的功能性大脑图像，根据疾病的严重程度将其分为五个阶段。目的是通过使用他们的功能性大脑图像来预测患者的PD疾病阶段。我们采用了以下预测方法：多元统计方法（线性判别分析，支持向量机，决策树和多层感知器[MLP]），集成学习模型（随机森林[RF]和自适应Boosting）以及深度卷积神经网络（ CNN）。对于统计模型和集成模型，采用了各种特征提取方法（主要成分分析[PCA]，多线性PCA，强度汇总统计[IStat]和Laws的纹理能量度量），以提取特征，合成少数样本过采样技术用于解决不平衡数据，并通过网格搜索找到了超参数的最佳组合。对于CNN建模，我们应用了图像增强技术来增加和平衡不同疾病阶段的数据大小。我们采用转移学习将预训练的VGG16权重和体系结构整合到模型拟合中，并且还测试了可以自动生成最佳神经体系结构的最新机器学习模型。我们发现IStat始终优于其他特征提取方法。MLP和RF分别是用于多元统计和集成学习模型的具有最高预测准确率的分析方法。总体，具有预先训练的VGG16权重和架构的深度CNN模型优于其他方法；它从成像中捕获了关键特征，有效地区分了正常对照组和PD患者，并实现了最高的分类精度。