A Graph-Based Framework with Patient Similarity Clustering, Imbalance-Aware Learning, and Multi-Level Explainability for Reliable Stroke-Risk Prediction

This research presents a reliable and interpretable framework for stroke-risk prediction by combining patient similarity graphs, clustering, graph attention networks, and imbalance-aware learning. Patients are represented as nodes in a mutual k-nearest-neighbor similarity graph, while Ward-linkage agglomerative clustering is used to capture community-level risk patterns. A two-layer Graph Attention Network with focal loss and an F2-based decision threshold is then used to improve the detection of stroke-positive patients, with particular emphasis on reducing false negatives. The framework also integrates GNNExplainer, SHAP, and counterfactual analysis to provide explanations at the patient, neighbor, and cluster levels. Experimental results show that the proposed GAT with agglomerative clustering provides stronger recall-oriented performance than the evaluated alternatives, while cross-validation, cluster stability, and explanation-consistency analyses provide additional evidence of reliability.