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Predicting Ischemic Stroke in Patients with Atrial Fibrillation Using Machine Learning

Authors :
Seonwoo Jung
Min-Keun Song
Eunjoo Lee
Sejin Bae
Yeon-Yong Kim
Doheon Lee
Myoung Jin Lee
Sunyong Yoo
Source :
Frontiers in Bioscience-Landmark, Vol 27, Iss 3, p 080 (2022)
Publication Year :
2022
Publisher :
IMR Press, 2022.

Abstract

Background: Atrial fibrillation (AF) is a well-known risk factor for stroke. Predicting the risk is important to prevent the first and secondary attacks of cerebrovascular diseases by determining early treatment. This study aimed to predict the ischemic stroke in AF patients based on the massive and complex Korean National Health Insurance (KNHIS) data through a machine learning approach. Methods: We extracted 65-dimensional features, including demographics, health examination, and medical history information, of 754,949 patients with AF from KNHIS. Logistic regression was used to determine whether the extracted features had a statistically significant association with ischemic stroke occurrence. Then, we constructed the ischemic stroke prediction model using an attention-based deep neural network. The extracted features were used as input, and the occurrence of ischemic stroke after the diagnosis of AF was the output used to train the model. Results: We found 48 features significantly associated with ischemic stroke occurrence through regression analysis (p-value < 0.001). When the proposed deep learning model was applied to 150,989 AF patients, it was confirmed that the occurrence ischemic stroke was predicted to be higher AUROC (AUROC = 0.727 ± 0.003) compared to CHA2DS2-VASc score (AUROC = 0.651 ± 0.007) and other machine learning methods. Conclusions: As part of preventive medicine, this study could help AF patients prepare for ischemic stroke prevention based on predicted stoke associated features and risk scores.

Details

Language :
English
ISSN :
27686701
Volume :
27
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Frontiers in Bioscience-Landmark
Publication Type :
Academic Journal
Accession number :
edsdoj.b87c44565144cc898e2deae61572182
Document Type :
article
Full Text :
https://doi.org/10.31083/j.fbl2703080