biostudies-literatureKim SHthe Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICTKorea University Grant20610https://www.ebi.ac.uk/biostudies/studies/S-EPMC8523653biostudies-literatureUnknown11(1)We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of theScientific reportsInterpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke.PMC8523653NRF-2020R1C1C1009294Jeon ETOh KKim JMPark JHKim GYu SKim CKPark MSKim BJBang OYSong TJKim JTJung JMKim SHSeo WKPark KYKim YJHwang YHChoi KHHeo SHChoi JCChung JWfalseInterpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke.We aimed to develop a novel prediction model for early neurological deterioration (END) based on an interpretable machine learning (ML) algorithm for atrial fibrillation (AF)-related stroke and to evaluate the prediction accuracy and feature importance of ML models. Data from multicenter prospective stroke registries in South Korea were collected. After stepwise data preprocessing, we utilized logistic regression, support vector machine, extreme gradient boosting, light gradient boosting machine (LightGBM), and multilayer perceptron models. We used the Shapley additive explanation (SHAP) method to evaluate feature importance. Of the 3,213 stroke patients, the 2,363 who had arrived at the hospital within 24 h of symptom onset and had available information regarding END were included. Of the2021-01-01T00:00:00Z2021 Oct2025-04-04T21:30:18.06Z2025-04-04T21:30:18.06ZS-EPMC85236533466387410.1038/s41598-021-99920-7