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

2025-12-20

AI Summary

To address challenges in diabetes clinical classification—including difficulty modeling patient relationships, poor generalization to unseen patients, and insufficient prediction interpretability—this paper proposes an Adaptive Patient-Centered Graph Neural Network (PC-GNN). Methodologically, it introduces a context-aware edge attention mechanism to capture clinically meaningful patient associations; incorporates confidence-guided node-graph feature fusion and neighborhood consistency regularization to balance individual specificity and population-level patterns; and pioneers a mini-graph construction strategy enabling real-time, interpretable predictions for unseen patients. Evaluated on a real-world hospital dataset from Algeria, PC-GNN significantly outperforms MLP, Random Forest, XGBoost, and GCN, achieving +3.2% test accuracy and +4.7% macro-F1 score. A companion Tkinter-based GUI system supports node-level confidence analysis and interactive clinical decision-making.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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 .

Problem

• Classifies diabetes using adaptive patient-centric graph neural networks
• Handles unseen patients with real-time explainable mini-graph predictions
• Outperforms traditional models on accuracy and interpretability for healthcare

Innovation

• Adaptive patient-centric GNN with context-aware attention
• Mini-graph approach for real-time explainable predictions
• Confidence-guided blending and neighborhood consistency regularization