Explainable Hybrid GRU–TabTransformer Learning with Cross-Attention and LLM-Assisted Interpretation for Stroke Risk Prediction

Early stroke risk prediction offers an opportunity for timely interventions and may help reduce the clinical burden associated with stroke. Artificial intelligence (AI) provides medical practitioners with tools to analyse clinical biomarkers and predict a patient’s stroke risk. However, existing models lack interpretability and explainable decision support, limiting their adoption in clinical settings. This paper proposes a hybrid Gated Recurrent Unit (GRU)-TabTransformer architecture using cross-attention for stroke-status prediction. The proposed architecture comprises two stages. In the first stage (Model A), ordered feature-sequence representations from a GRU encoder are combined with concatenated categorical and numerical tabular features from a TabTransformer encoder. The model passes these distinct learned representations through cross-attention and linear projection layers before the final prediction. In the second stage (Model B), we augment our model with Large Language Models (LLMs) and Local Interpretable Model-agnostic Explanations (LIME) to provide per-sample, post hoc, human-interpretable explanations based on predicted probabilities. Experimental results show that both models achieve competitive sensitivity and accuracy values. In particular, the Synthetic Minority Over-sampling Technique (SMOTE) yielded a more balanced sensitivity–specificity trade-off, with a sensitivity of 74.00% and a specificity of 75.10%. Moreover, the model achieved an accuracy of 75.05% with SMOTE. An ablation study further shows that Cross-Attention offers better sensitivity and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC), while Gated Fusion performs better on several other metrics. Additionally, age and average glucose level were the most influential stroke risk indicators, while Body Mass Index (BMI) and ever-married status were secondary model-attributed features. A two-factor repeated-measures Analysis of Variance (ANOVA) confirmed an interaction between model choice and the class-balancing technique used in stroke risk prediction systems. The Mistral + Hybrid GRU-TabTransformer architecture also recorded a mean inference time of 57.59s using few-shot prompting. Overall, the results provide a proof of concept for integrating hybrid GRU-TabTransformer with cross-attention and LLM-based explainability to support interpretable stroke risk prediction systems, pending robust external validation before deployment.