Development and validation of explainable machine learning models for predicting 3-month functional outcomes in acute ischemic stroke: a SHAP-based approach.

Objective: To develop and validate explainable machine learning models for predicting 3-month functional outcomes in acute ischemic stroke (AIS) patients using SHapley Additive exPlanations (SHAP) framework. Methods: This retrospective cohort study included 538 AIS patients admitted within 72 h of symptom onset. Patients were randomly divided into training (70%) and validation (30%) sets. Clinical, laboratory, and imaging data were collected. Least Absolute Shrinkage and Selection Operator regression was used for feature selection. Five machine learning models were developed: support vector machine, Results: Among 538 patients (mean age 68.5 ± 12.7 years, 58.0% male), 34.2% had poor 3-month outcomes (mRS 3-6). The GBM achieved the best predictive performance with AUC of 0.91, accuracy of 0.81, sensitivity of 0.95, and specificity of 0.61 in validation set, significantly outperforming logistic regression (AUC = 0.78). The model demonstrated excellent calibration and superior net benefit in decision curve analysis across threshold probabilities of 0.1-0.7. SHAP analysis identified admission NIHSS score (30.8%), age (14.9%), and ASPECTS ≥7 (13.7%) as the most influential predictors, with neutrophil-to-lymphocyte ratio (10.1%) and platelet distribution width (9.7%) also contributing significantly to outcome prediction. Conclusion: Explainable machine learning models can accurately predict 3-month functional outcomes in AIS patients. The SHAP framework enhances model transparency, addressing interpretability barriers for clinical implementation while maintaining superior predictive performance.