@article{5fd742f6eec34e378cd897af423715d3,
title = "Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke",
abstract = "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 these, 318 (13.5%) had END. The LightGBM model showed the highest area under the receiver operating characteristic curve (0.772; 95% confidence interval, 0.715–0.829). The feature importance analysis revealed that fasting glucose level and the National Institute of Health Stroke Scale score were the most influential factors. Among ML algorithms, the LightGBM model was particularly useful for predicting END, as it revealed new and diverse predictors. Additionally, the effects of the features on the predictive power of the model were individualized using the SHAP method.",
author = "Kim, {Seong Hwan} and Jeon, {Eun Tae} and Sungwook Yu and Kyungmi Oh and Kim, {Chi Kyung} and Song, {Tae Jin} and Kim, {Yong Jae} and Heo, {Sung Hyuk} and Park, {Kwang Yeol} and Kim, {Jeong Min} and Park, {Jong Ho} and Choi, {Jay Chol} and Park, {Man Seok} and Kim, {Joon Tae} and Choi, {Kang Ho} and Hwang, {Yang Ha} and Kim, {Bum Joon} and Chung, {Jong Won} and Bang, {Oh Young} and Gyeongmoon Kim and Seo, {Woo Keun} and Jung, {Jin Man}",
note = "Funding Information: The authors disclose receipt of the following financial support for the research, authorship, and publication of this article: the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2020R1C1C1009294) and Korea University Grant. The funders had no role in the study design; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Funding Information: The authors declare the following potential conflicts of interest with respect to the research, authorship, and publication of this article: J-M Jung has received lecture honoraria from Pfizer, Sanofi-Aventis, Ostuka, Dong-A, and Hanmi Pharmaceutical Co., Ltd; consulting fees from Daewoong Pharmaceutical Co., Ltd. WK Seo received honoraria for lectures from Pfizer, Sanofi-Aventis, Otsuka Korea, Dong-A Pharmaceutical Co., Ltd., Beyer, Daewoong Pharmaceutical Co. Ltd., Daiichi Sankyo Korea Co., Ltd., and Boryung Pharmaceutical Co., Ltd.; a study grant from Daiichi Sankyo Korea Co., Ltd.; and consulting fees from OBELAB Inc. All other authors have no coompeting interest. Publisher Copyright: {\textcopyright} 2021, The Author(s).",
year = "2021",
month = dec,
doi = "10.1038/s41598-021-99920-7",
language = "English",
volume = "11",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",
}