Predicting Ischemic Stroke in Patients with Atrial Fibrillation Using Machine Learning

Frontiers in Bioscience-Landmark

Yeon-Yong Kim¹  Sejin Bae¹  Eunjoo Lee¹  Doheon Lee²  Myoung Jin Lee³  Sunyong Yoo³  Seonwoo Jung³  Min-Keun Song⁴ 

[1] Big Data Steering Department, National Health Insurance Service, 26464 Wonju, Republic of Korea;Bio-Synergy Research Center, 34141 Daejeon, Republic of Korea;Department of ICT Convergence System Engineering, Chonnam National University, 61186 Gwangju, Republic of Korea;Department of Physical & Rehabilitation Medicine, Chonnam National University Medical School & Hospital, 61469 Gwangju, Republic of Korea;

关键词: atrial fibrillation;    stroke;    national health insurance service;    machine learning;    deep neural network;    attention;   

DOI  :  10.31083/j.fbl2703080

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown