27.08.2026journal-article
Artificial Intelligence in Health
An explainable deep learning-based computational framework for stroke prediction using multimodal radiomics features: A retrospective machine learning study
Stroke prediction models (SPMs) serve two important roles: accurately recognizing stroke symptoms and informing treatment decisions. The Synthetic Minority Oversampling Technique is typically used for analysis. An improved deep learning model for multimodal radiomic features and data preprocessing on imbalanced datasets is recommended. The chaotic map-guided approach (CMGA) has been applied to generate a feature-importance score, designed to improve the predictability and performance of a traditional model for feat …