Alzheimer's Disease Diagnosis via Deep Factorization Machine Models
This work addresses the need for interpretable models in medical diagnosis for Alzheimer's Disease, offering an incremental improvement by combining existing techniques.
The paper tackled Alzheimer's Disease diagnosis by proposing a Deep Factorization Machine model to classify patients while extracting knowledge about biomarker interactions, achieving more accurate classification than competing models on data from the Alzheimer's Disease Neuroimaging Initiative.
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.