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1
A multidimensional machine learning model was developed to predict early neurological deterioration (END) in acute ischemic stroke patients.
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2
Five core predictors for END were identified: NIHSS score, blood glucose, infarct core volume, collateral circulation status, and neutrophil-to-lymphocyte ratio.
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3
The Random Forest model outperformed K-Nearest Neighbors and Gradient Boosting Machine in predicting END, with AUC values of 0.779 and 0.775.
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4
Admission NIHSS score, blood glucose, infarct core volume, and NLR were independent risk factors for poor prognosis in AIS patients.
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The study highlights the need for advanced predictive models to facilitate early identification and intervention for high-risk AIS patients.
