  Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by
rapid and irregular contractions of the atria. It significantly elevates the
risk of strokes due to slowed blood flow in the atria, especially in the left
atrial appendage, which is prone to blood clot formation. Such clots can
migrate into cerebral arteries, leading to ischemic stroke. To assess whether
AF patients should be prescribed anticoagulants, doctors often use the
CHA2DS2-VASc scoring system. However, anticoagulant use must be approached with
caution as it can impact clotting functions. This study introduces a machine
learning algorithm that predicts whether patients with AF should be recommended
anticoagulant therapy using 12-lead ECG data. In this model, we use STOME to
enhance time-series data and then process it through a Convolutional Neural
Network (CNN). By incorporating a path development layer, the model achieves a
specificity of 30.6% under the condition of an NPV of 1. In contrast, LSTM
algorithms without path development yield a specificity of only 2.7% under the
same NPV condition.
