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Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study

Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study

We focused on model performance comparison, ranking of feature importance, and explanation of model predictions. We leveraged the SHapley Additive ex Planations (SHAP) to depict the stroke prediction models and guarantee that the models predict with a solid basis. For imbalanced prediction targets, preprocessing was performed with different resampling methods to balance the data set before model performance comparisons.

Po-Yuan Su, Yi-Chia Wei, Hao Luo, Chi-Hung Liu, Wen-Yi Huang, Kuan-Fu Chen, Ching-Po Lin, Hung-Yu Wei, Tsong-Hai Lee

JMIR Med Inform 2022;10(3):e32508