  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.
