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Research Article
Jin-Man Jung
Korea University Zebrafish Translational Medical Research Center
Corresponding Author
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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 multi-center 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 explanations (SHAP) method to evaluate feature importance. Of the 3,623 stroke patients, the 2,363 who had arrived at the hospital within 24 hours 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 (0.778, 95% CI, 0.726 - 0.830). 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 SHAP method can be adjusted to individualize the features’ effects on the predictive power of the model.
Keywords
early neurological deterioration (END), machine learning (ML), atrial fibrillation (AF)-related stroke
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Competing interest reported. 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 conflict of interest
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Supplemental Materials. Expanded Methods: Online Tables I – IV, Online Figures I – III
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This preprint is under consideration at Scientific Reports. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Jin-Man Jung
Korea University Zebrafish Translational Medical Research Center
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 07 May, 2021
No community comments so far
Editorial decision: Major revision
On 26 Jul, 2021
Reviews received
Received 06 Jul, 2021
Reviewers agreed
On 19 Jun, 2021
Reviewers invited
Invitations sent on 23 May, 2021
Editor assigned
On 18 May, 2021
Editor invited
On 07 May, 2021
Submission checks complete
On 06 May, 2021
First submitted
On 21 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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 multi-center 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 explanations (SHAP) method to evaluate feature importance. Of the 3,623 stroke patients, the 2,363 who had arrived at the hospital within 24 hours 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 (0.778, 95% CI, 0.726 - 0.830). 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 SHAP method can be adjusted to individualize the features’ effects on the predictive power of the model.
Figure 1
Figure 2
Figure 3
Figure 4
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