APC-GNN++: An Adaptive Patient-Centric GNN with Context-Aware Attention and Mini-Graph Explainability for Diabetes Classification

TL;DR

APC-GNN++ is a novel adaptive graph neural network designed for diabetes classification that incorporates context-aware attention, explainability, and a mini-graph approach for real-time predictions and interpretability.

Contribution

It introduces a patient-centric GNN with context-aware attention and mini-graph explainability, enabling real-time, interpretable predictions for unseen patients.

Findings

Outperforms traditional ML models and vanilla GCN in accuracy and F1 score.

Provides interpretable patient insights through confidence scores.

Enables real-time predictions for new patients without retraining.

Abstract

We propose APC-GNN++, an adaptive patient-centric Graph Neural Network for diabetes classification. Our model integrates context-aware edge attention, confidence-guided blending of node features and graph representations, and neighborhood consistency regularization to better capture clinically meaningful relationships between patients. To handle unseen patients, we introduce a mini-graph approach that leverages the nearest neighbors of the new patient, enabling real-time explainable predictions without retraining the global model. We evaluate APC-GNN++ on a real-world diabetes dataset collected from a regional hospital in Algeria and show that it outperforms traditional machine learning models (MLP, Random Forest, XGBoost) and a vanilla GCN, achieving higher test accuracy and macro F1- score. The analysis of node-level confidence scores further reveals how the model balances self-information and graph-based evidence across different patient groups, providing interpretable patient-centric insights. The system is also embedded in a Tkinter-based graphical user interface (GUI) for interactive use by healthcare professionals .