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

• URL: http://arxiv.org/abs/2512.18473v1
• Date: Sat, 20 Dec 2025 19:12:45 GMT
• Title: APC-GNN++: An Adaptive Patient-Centric GNN with Context-Aware Attention and Mini-Graph Explainability for Diabetes Classification
• Authors: Khaled Berkani,
• Abstract summary: APC-GNN++ is an adaptive patient-centric Graph Neural Network for diabetes classification.<n>We evaluate APC-GNN++ on a real-world diabetes dataset collected from a regional hospital in Algeria.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 .

Related papers

• Beyond Traditional Diagnostics: Transforming Patient-Side Information into Predictive Insights with Knowledge Graphs and Prototypes [55.310195121276074]
  We propose a Knowledge graph-enhanced, Prototype-aware, and Interpretable (KPI) framework to predict diseases.<n>It integrates structured and trusted medical knowledge into a unified disease knowledge graph, constructs clinically meaningful disease prototypes, and employs contrastive learning to enhance predictive accuracy.<n>It provides clinically valid explanations that closely align with patient narratives, highlighting its practical value for patient-centered healthcare delivery.
  arXiv  Detail & Related papers  (2025-12-09T05:37:54Z)
• MIRNet: Integrating Constrained Graph-Based Reasoning with Pre-training for Diagnostic Medical Imaging [67.74482877175797]
  MIRNet is a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning.<n>We introduce TongueAtlas-4K, a benchmark comprising 4,000 images annotated with 22 diagnostic labels.
  arXiv  Detail & Related papers  (2025-11-13T06:30:41Z)
• Interpretable Clinical Classification with Kolgomorov-Arnold Networks [70.72819760172744]
  Kolmogorov-Arnold Networks (KANs) offer intrinsic interpretability through transparent, symbolic representations.<n>KANs support built-in patient-level insights, intuitive visualizations, and nearest-patient retrieval.<n>These results position KANs as a promising step toward trustworthy AI that clinicians can understand, audit, and act upon.
  arXiv  Detail & Related papers  (2025-09-20T17:21:58Z)
• Similarity-Based Self-Construct Graph Model for Predicting Patient Criticalness Using Graph Neural Networks and EHR Data [0.46040036610482665]
  We propose a Similarity-Based Self-Construct Graph Model (SBSCGM) that builds a patient similarity graph from multi-modal EHR data.<n>A HybridGraphMedGNN architecture operates on this graph to predict patient mortality and a continuous criticalness score.<n>Our framework offers a scalable and interpretable solution for critical care risk prediction, with potential to support clinicians in real-world ICU deployment.
  arXiv  Detail & Related papers  (2025-08-01T13:25:04Z)
• Graph Neural Networks for Heart Failure Prediction on an EHR-Based Patient Similarity Graph [1.4260605984981949]
  This study introduces a novel approach using graph neural networks (GNNs) and a Graph Transformer (GT) to predict the incidence of heart failure (HF)<n>Three models - GraphSAGE, Graph Attention Network (GAT), and Graph Transformer (GT) - were implemented to predict HF incidence.<n>The GT model demonstrated the best performance (F1 score: 0.5361, AUROC: 0.7925, AUPRC: 0.5168)
  arXiv  Detail & Related papers  (2024-11-29T14:40:19Z)
• A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network [0.0]
  Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare.<n>Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC.<n>We propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data.
  arXiv  Detail & Related papers  (2024-09-20T17:26:38Z)
• Graph AI in Medicine [9.733108180046555]
  Graph neural networks (GNNs) process data holistically by viewing modalities as nodes interconnected by their relationships. GNNs capture information through localized neural transformations defined on graph relationships. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge.
  arXiv  Detail & Related papers  (2023-10-20T19:01:01Z)
• Graph data modelling for outcome prediction in oropharyngeal cancer patients [38.37247384790338]
  Graph neural networks (GNNs) are becoming increasingly popular in the medical domain for the tasks of disease classification and outcome prediction. We propose a patient hypergraph network (PHGN) which has been investigated in an inductive learning setup for binary outcome prediction in oropharyngeal cancer (OPC) patients.
  arXiv  Detail & Related papers  (2023-10-04T16:09:35Z)
• A Meta-GNN approach to personalized seizure detection and classification [53.906130332172324]
  We propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We train a Meta-GNN based classifier that learns a global model from a set of training patients. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients.
  arXiv  Detail & Related papers  (2022-11-01T14:12:58Z)
• A Graph-Enhanced Click Model for Web Search [67.27218481132185]
  We propose a novel graph-enhanced click model (GraphCM) for web search. We exploit both intra-session and inter-session information for the sparsity and cold-start problems.
  arXiv  Detail & Related papers  (2022-06-17T08:32:43Z)
• IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction [47.999621481852266]
  We propose an interpretable graph learning-based model which interprets the clinical relevance of the input features towards the task. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. Our proposed model shows superior performance with respect to compared methods with an increase in an average accuracy of 3.2% for Tadpole, 1.6% for UKBB Gender, and 2% for the UKBB Age prediction task.
  arXiv  Detail & Related papers  (2021-03-29T13:04:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.