Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.

翻译：机器学习方法在阿尔茨海默病（AD）的自动早期诊断中展现出巨大潜力。然而，部分基于影像数据的机器学习方法可解释性较差，因其决策过程通常不明确。可解释提升机（EBM）是基于广义加性模型统计框架的可解释机器学习模型，但目前仅用于表格数据。为此，我们提出一种结合EBM优势与基于深度学习特征提取的高维影像数据的框架。该框架具有可解释性，能够提供每个特征的重要性。我们在阿尔茨海默病神经影像学倡议（ADNI）数据集上验证该框架，AD与对照组分类的准确率达0.883，曲线下面积（AUC）达0.970。进一步在外部测试集上验证，AD与主观认知下降（SCD）分类的准确率达0.778，AUC达0.887。与采用体积生物标志物替代深度学习特征的EBM模型以及具有优化架构的端到端卷积神经网络（CNN）相比，该框架表现显著更优。