期刊
FRONTIERS IN BIOSCIENCE-LANDMARK
卷 27, 期 3, 页码 -出版社
IMR PRESS
DOI: 10.31083/j.fbl2703080
关键词
atrial fibrillation; stroke; national health insurance service; machine learning; deep neural network; attention
资金
- Bio-Synergy Research Project of the Ministry of Science, ICT, and Future Planning, through the National Research Foundation [2012M3A9C4048758]
- National Research Foundation of Korea - Korea government (MSIT) [NRF-2020R1C1C1006007]
This study aims to predict the occurrence of ischemic stroke in AF patients using machine learning methods on a large dataset. The study finds 48 features that are significantly associated with ischemic stroke occurrence through regression analysis. The proposed deep learning model outperforms traditional scoring methods and other machine learning methods in predicting ischemic stroke in AF patients.
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 (KNH1S) 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 KNHES. 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 CHA(2)DS(2)-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.
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