  The current state-of-the-art deep neural networks (DNNs) for Alzheimer's
Disease diagnosis use different biomarker combinations to classify patients,
but do not allow extracting knowledge about the interactions of biomarkers.
However, to improve our understanding of the disease, it is paramount to
extract such knowledge from the learned model. In this paper, we propose a Deep
Factorization Machine model that combines the ability of DNNs to learn complex
relationships and the ease of interpretability of a linear model. The proposed
model has three parts: (i) an embedding layer to deal with sparse categorical
data, (ii) a Factorization Machine to efficiently learn pairwise interactions,
and (iii) a DNN to implicitly model higher order interactions. In our
experiments on data from the Alzheimer's Disease Neuroimaging Initiative, we
demonstrate that our proposed model classifies cognitive normal, mild cognitive
impaired, and demented patients more accurately than competing models. In
addition, we show that valuable knowledge about the interactions among
biomarkers can be obtained.
