Immediate Recurrence of Atrial Fibrillation (IRAF) occurs after synchronized cardioversion with a prevalence of 5-26%. [1] Recurrence decreases cardioversion efficacy and puts patients through the unnecessary risk of unsuccessful cardioversion. Better knowledge of patient susceptibility factors will allow clinicians to pre-treat these patients to reduce IRAF incidence. This project aims to shed more light on IRAF susceptibility factors through analysis of a cardioversion database.
- Cardioversion Database (not yet publicly avaliable)
- Gradient Boost Machine (XGBoost)
- Deep Learning (TensorFlow, ...)
- Verification with scikit-learn (Random Forest, ...)
- Planned
- kernel Support Vector Machine (and other supervised learning to seperate susceptibility groups)
- Clustering