†These authors contributed equally.
Academic Editor: Alexandros G. Georgakilas
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