基于患者中心图神经网络，实现糖尿病分类与可解释预测。
APC-GNN++: An Adaptive Patient-Centric GNN with Context-Aware Attention and Mini-Graph Explainability for Diabetes Classification
- 动态关注患者间临床相关性，融合节点特征与图结构信息
- 新患者预测无需重训练，准确率与宏平均F1均超基准模型
- 支持实时可解释分析，适合临床医生交互使用
我们提出APC-GNN++，一种面向糖尿病分类的自适应患者中心图神经网络。模型融合上下文感知边注意力、置信度引导的节点特征与图表示融合，以及邻域一致性正则化，更精准捕捉患者间的临床有意义关联。为应对未见患者，引入最小图方法，利用新患者最近邻构建局部图，实现实时可解释预测且无需重训练全局模型。在阿尔及利亚某地区医院的真实糖尿病数据集上评估，结果优于MLP、随机森林、XGBoost及基础GCN，测试准确率和宏平均F1分数均更高。节点置信度分析揭示了模型在不同患者群体中对自我信息与图证据的平衡机制，提供可解释的患者中心洞察。系统已集成至基于Tkinter的图形用户界面，支持医疗专业人员交互操作。
原文摘要 · Abstract (English)
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 .
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