GraphX-Net: A Graph Neural Network-Based Shapley Values for Predicting Breast Cancer Occurrence

Journal article


Basaad, A., Basurra, S., Vakaj, E. and Aleskandarany, M. 2024. GraphX-Net: A Graph Neural Network-Based Shapley Values for Predicting Breast Cancer Occurrence. IEEE Access. 12, pp. 93993 - 94007. https://doi.org/https://doi.org/10.1109/ACCESS.2024.3424526
AuthorsBasaad, A., Basurra, S., Vakaj, E. and Aleskandarany, M.
Abstract

Breast cancer is a major health problem worldwide, and an accurate prediction of its recurrence is crucial to early detection of recurrence and personalized treatment. In recent years, various AI techniques have been applied to predict cancer recurrence with increasingly high accuracy. Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing structured data, including knowledge networks. In this study, we explore the application of GNN-based node classification to predict the occurrence of relapse status in breast cancer patients. We propose a noval model, namely GraphX-Net, Shapley Value-based Graph Neural Network. GraphX-Net leverages a graph representation by analyzing public breast cancer gene expression data, where nodes represent patients and edges capture relationships between them based on various factors such as tumor cellularity, histological subtype, hormone therapy, patient vital status, primary tumor laterality, type of breast surgery, and other clinicopathological parameters. Our approach integrates diverse data sources and utilizes graph convolutional layers to generate informative node embeddings. The model uniquely calculates node feature contributions using Shapley values, sets nodes’ thresholds, and considers the total node neighboring effects. These innovations enable GraphX-Net to achieve state-of-the-art performance in predicting breast cancer recurrence, highlighting its potential as a powerful tool for improving patient outcomes.

KeywordsBreast cancer ; Graph neural networks; XAI; node classification; occurrence prediction
Year2024
JournalIEEE Access
Journal citation12, pp. 93993 - 94007
PublisherIEEE
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/https://doi.org/10.1109/ACCESS.2024.3424526
Web address (URL)https://ieeexplore.ieee.org/document/10587228
Accepted author manuscript
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Open
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online08 Jul 2024
Publication process dates
Deposited05 Aug 2024
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https://repository.derby.ac.uk/item/q7909/graphx-net-a-graph-neural-network-based-shapley-values-for-predicting-breast-cancer-occurrence

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