Atrial fibrillation (AF) is the most common irregular heartbeat among the world’s population and is a major contributing factor to clot formation within the heart. When such a blood clot enters the cardiovascular system, it first must travel along the ascending aorta. The clot may travel along the aortic arch and travel towards the brain through the left and right common carotid arteries. If clot enters these vessels, it can become lodged within the smaller vessels of the brain and cause a stroke. We apply supervised machine learning classifiers (logit, SVM) for detecting stroke probability using simulation data. Various scenarios are implemented to examine the impact of variables such as shape of the aortic arch, varying clot dimensions and the entry point. Model selection tools (grid search, cross-validation) and classification probability are calculated for each classifier. Application will be shown using Jupyter notebooks.