Uncertainty-aware evidential deep learning for reliable stroke detection and clinical decision support

High discriminative performance alone does not ensure reliable clinical decision support; well-calibrated confidence estimates are also essential. Standard deterministic deep learning classifiers produce probability predictions without explicitly quantifying predictive uncertainty, limiting confidence assessment for safety-critical clinical applications. We propose an uncertainty-aware evidential deep learning framework for binary stroke prediction based on the Dirichlet distribution. This framework estimates predictive confidence in a single pass, with explicit decomposition of epistemic and aleatoric uncertainty, characterizing model and data uncertainty respectively. The model achieved strong discriminative performance, with an area under the receiver operating characteristic curve approaching 0.99 and high average precision in distinct experimental repeats. At the operating point selected using Youden’s J statistic, the model achieved a sensitivity of 0.968, a specificity of 0.949 and a negative predictive value of 0.980. The calibration analysis demonstrates that predicted probabilities are closer to the observed outcomes, while the uncertainty analysis indicates that ambiguous and misclassified cases exhibit higher epistemic uncertainty, conveniently identifying them as low-confidence predictions. Through comprehensive ablation studies, the proposed full evidential formulation consistently outperforms the baseline convolutional networks, focal loss and Monte Carlo dropout. Computational evaluation showed that evidential inference required a single forward pass, whereas MC Dropout used 10 stochastic forward passes per prediction. The results show that evidential deep learning correctly identifies and calibrates uncertainty, providing clinically useful stroke detection at a lower computational cost, which is ideal for any clinical decision support system.