# Self-Attention Equipped Graph Convolutions for Disease Prediction

## TL;DR

This paper introduces a graph convolutional model with a novel self-attention layer that effectively leverages multi-modal medical data for improved disease prediction, demonstrating superior accuracy and speed over existing methods.

## Contribution

The paper presents a new self-attention mechanism integrated into graph convolutions to better utilize multi-modal data for disease prediction.

## Key findings

- Outperforms state-of-the-art methods in accuracy
- Provides faster computational performance
- Effectively models multi-modal data relationships

## Abstract

Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such diverse data gives complementary information about the patient\'s condition to make an informed diagnosis. A model capable of leveraging the individuality of each multi-modal data is required for better disease prediction. We propose a graph convolution based deep model which takes into account the distinctiveness of each element of the multi-modal data. We incorporate a novel self-attention layer, which weights every element of the demographic data by exploring its relation to the underlying disease. We demonstrate the superiority of our developed technique in terms of computational speed and performance when compared to state-of-the-art methods. Our method outperforms other methods with a significant margin.