Abstract
The current state-of-the-art deep neural networks (DNNs) for Alzheimer’s Diseasediagnosis use different biomarker combinations to classify patients, but do notallow extracting knowledge about the interactions of biomarkers. However, toimprove our understanding of the disease, it is paramount to extract suchknowledge 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.
Publication
Machine Learning in Medical Imaging