Composite Deep-Learning Model for 90-Day mRS Prediction in Post-Stroke Patients

Abstract

To counteract small sample size, severe class imbalance and high feature redundancy in 90-day mRS prediction after stroke, this study proposes a four-stage pipeline— “ADASYN re-sampling → clinical + statistical feature screening → dimensionality reduction → 5-fold cross-validation” —and benchmark composite deep-learning architectures. ADASYN first balances the minority classes in the original feature space. Next, a tri-level filter (clinical domain knowledge, variance threshold, mutual information) removes clinically meaningless or redundant variables, after which PCA compresses the remaining features while preserving critical neurological signatures (e.g., brain-herniation history). Four hybrid CNN–RNN models are trained and compared under strict 5-fold cross-validation; the optimal ensemble yields stable, clinically interpretable probabilities that can support individualized rehabilitation planning.

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