Predicting atrial fibrillation in comorbid patients with arterial hypertension and chronic obstructive pulmonary disease using laboratory research methods: a machine learning approach - Evgeniya V. Kazantseva; Aleksander A. Ivannikov; Aida I. Tarzimanova; Valeriy I. Podzolkov (Digital Diagnostics, 2024)
BACKGROUND: Arterial hypertension and chronic obstructive pulmonary disease have a deleterious effect on the structure of the heart, leading to the development of atrial fibrillation, which remains the leading cause of cerebral stroke and premature death [1]. Consequently, the early identification of atrial fibrillation risk factors in patients with arterial hypertension and chronic obstructive pulmonary disease is of paramount importance for the prevention of such conditions. This is why predictive cardiology employs machine learning methods, which are demonstrably superior to classical statistical methods of prediction [2–4].