Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completion

TL;DR

This paper introduces a novel geometric matrix completion method using graph-structured data and neural networks to improve disease classification accuracy in incomplete multi-modal datasets, demonstrated on Alzheimer's disease prediction.

Contribution

It proposes a new approach combining geometric matrix completion with graph neural networks for multi-modal disease classification with missing data.

Findings

Achieved 0.950 AUC and 87% accuracy on ADNI TADPOLE dataset.

Outperformed traditional classifiers and recent GCN-based methods.

Effectively handled incomplete multi-modal medical data.

Abstract

In large population-based studies and in clinical routine, tasks like disease diagnosis and progression prediction are inherently based on a rich set of multi-modal data, including imaging and other sensor data, clinical scores, phenotypes, labels and demographics. However, missing features, rater bias and inaccurate measurements are typical ailments of real-life medical datasets. Recently, it has been shown that deep learning with graph convolution neural networks (GCN) can outperform traditional machine learning in disease classification, but missing features remain an open problem. In this work, we follow up on the idea of modeling multi-modal disease classification as a matrix completion problem, with simultaneous classification and non-linear imputation of features. Compared to methods before, we arrange subjects in a graph-structure and solve classification through geometric matrix completion, which simulates a heat diffusion process that is learned and solved with a recurrent neural network. We demonstrate the potential of this method on the ADNI-based TADPOLE dataset and on the task of predicting the transition from MCI to Alzheimer's disease. With an AUC of 0.950 and classification accuracy of 87%, our approach outperforms standard linear and non-linear classifiers, as well as several state-of-the-art results in related literature, including a recently proposed GCN-based approach.