Predicting Post-Stroke Functional Outcome Using Explainable Machine Learning and Integrated Data

Abstract Functional outcome after acute ischemic stroke (AIS) varies widely, and existing prognostic scores may not capture complex relationships. We evaluated a diverse set of clinical characteristics and blood biomarkers with multiple machine learning models to predict 3-month functional outcome after AIS, and used explainable artificial intelligence to identify key drivers of performance. Models were trained on 506 patients aged 18–69 years with AIS enrolled at four stroke units in western Sweden. We compared extreme gradient boosting, multilayer perceptron (MLP), and L1- and L2-regularized logistic regression. Feature importance was assessed with Shapley additive global explanations. Of the 506 patients, 105 had an unfavorable outcome (modified Rankin Scale score > 2). All models showed high area under the curve (AUROC, 0.900–0.906). The MLP achieved the highest precision–recall performance (AUPRC, 0.773 ± 0.080) and sensitivity (0.655 ± 0.096), though with lower specificity (0.920 ± 0.035). Stroke severity (NIH Stroke Scale score) was the dominant predictor across models. Among biomarkers, brain-derived tau (BD-tau) was most informative, followed by inflammation-related plasma proteins. In conclusion, machine learning accurately predicted functional outcome after AIS. BD-tau and inflammation-related proteins contributed predictive information above stroke severity, suggesting a potential for blood biomarkers to enhance individualized prognostication after AIS.