Interpretable Graph Convolutional Networks for cardiovascular disease risk prediction in patients with Type 2...

Cardiovascular disease (CVD) is the most prevalent complication of Type 2 Diabetes Mellitus (T2DM) and a leading cause of mortality in this population. Early and accurate CVD risk prediction is essential for timely intervention, yet traditional clinical risk calculators may overlook complex, non-linear relationships between risk factors, and have exhibited varying performance. Graph neural networks (GNNs) are able to capture these complex relationships but often lack interpretability.

The present study aims to develop and evaluate the first interpretable Graph Neural Network (GNN)-based framework for Cardiovascular Disease (CVD) risk prediction in patients with Type 2 Diabetes Mellitus (T2DM). We introduce a novel approach that integrates a GNN classifier with a rule-based surrogate model to generate clinically meaningful explanations for the model's predictions, addressing the critical need for both high accuracy and transparency in clinical AI.

A population graph of 560 T2DM patients was constructed using demographic, lifestyle, laboratory, and treatment data. A GNN-based classifier was trained by leveraging a loss function originally designed for graph-based anomaly detection to address class imbalance. Post-hoc interpretability was achieved through the deployment of a RuleFit surrogate model, combining decision tree ensembles and a sparse linear model to extract global, rule-based explanations.

The proposed model achieved an AUC of 0.786±0.076, exceeding all benchmark methods and outperforming prior best-reported results on this dataset by over 7% in terms of the AUC, and produced well-calibrated probabilities (Brier score: 0.053±0.021). RuleFit explanations aligned with established CVD risk factors, while revealing intermediate-risk patterns, such as residual dyslipidemia despite treatment, that may warrant earlier intervention.

The proposed interpretable GNN framework demonstrated its ability to provide reliable CVD risk estimates while offering transparent, clinically relevant explanations. These findings support its potential integration into CVD risk screening tools for patients with T2DM, paving the way for real-world clinical implementation.