  Thyrotoxicosis (TT) is associated with an increase in both total and
cardiovascu-lar mortality. One of the main thyrotoxicosis risks is Atrial
Fibrillation (AF). Right AF predicts help medical personal prescribe the
correct medicaments and correct surgical or radioiodine therapy. The main goal
of this study is creating a method for practical treatment and diagnostic AF.
This study proposes a new method for assessing the risk of occurrence atrial
fibrillation for patients with TT. This method considers both the features of
the complication and the specifics of the chronic disease. A model is created
based on case histories of patients with thyrotoxicosis. We used Machine
Learning methods for creating several models. Each model has advantages and
disadvantages depending on the diagnostic and medical purposes. The resulting
models show high results in the different metrics of the prediction of AF.
These models interpreted and simple for use. Therefore, models can be used as
part of the support and decision-making system (DSS) by medical specialists in
the treatment and diagnostic of AF.
