Alzheimer's Disease Diagnosis via Deep Factorization Machine Models

TL;DR

This paper introduces a Deep Factorization Machine model for Alzheimer's diagnosis that combines deep learning with interpretability, enabling accurate classification and understanding of biomarker interactions.

Contribution

It presents a novel hybrid model that captures complex biomarker interactions while maintaining interpretability, improving diagnosis accuracy over existing methods.

Findings

The model outperforms competing models in classification accuracy.

It enables extraction of meaningful biomarker interaction knowledge.

Demonstrates effectiveness on Alzheimer's Neuroimaging data.

Abstract

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.