A weighted patient network-based framework for predicting chronic diseases using graph neural networks.
Journal:
Scientific reports
Published Date:
Nov 19, 2021
Abstract
Chronic disease prediction is a critical task in healthcare. Existing studies fulfil this requirement by employing machine learning techniques based on patient features, but they suffer from high dimensional data problems and a high level of bias. We propose a framework for predicting chronic disease based on Graph Neural Networks (GNNs) to address these issues. We begin by projecting a patient-disease bipartite graph to create a weighted patient network (WPN) that extracts the latent relationship among patients. We then use GNN-based techniques to build prediction models. These models use features extracted from WPN to create robust patient representations for chronic disease prediction. We compare the output of GNN-based models to machine learning methods by using cardiovascular disease and chronic pulmonary disease. The results show that our framework enhances the accuracy of chronic disease prediction. The model with attention mechanisms achieves an accuracy of 93.49% for cardiovascular disease prediction and 89.15% for chronic pulmonary disease prediction. Furthermore, the visualisation of the last hidden layers of GNN-based models shows the pattern for the two cohorts, demonstrating the discriminative strength of the framework. The proposed framework can help stakeholders improve health management systems for patients at risk of developing chronic diseases and conditions.
Authors
Keywords
Algorithms
Cardiovascular Diseases
Chronic Disease
Data Interpretation, Statistical
Databases, Factual
Female
Humans
Machine Learning
Male
Neural Networks, Computer
Programming Languages
Pulmonary Disease, Chronic Obstructive
Reproducibility of Results
Risk
Software
Translational Research, Biomedical