This article discusses the issue of assessing the quality of predicting the dynamics of the human body in conditions of cardiovascular disease using intelligent software systems. To improve the forecast accuracy, the voting method of 3 competing systems was used, as well as the elimination of sparse data columns. Assessment of the quality of the prognosis of complications of cardiovascular diseases is carried out in terms of the accuracy and specificity of the diagnosis. The constructed system for 10 predicted diagnoses out of 12 showed a prediction accuracy of more than 90% with a specificity of more than 85%. This result shows a fairly high predictive ability of the created system when solving the problem of predicting the reaction of the human body to the onset of cardiovascular diseases (for example, complications of myocardial infarction).

The article solves the problem of creating models for predicting the course and complications of cardiovascular diseases. Artificial neural networks based on the Keras library are used. The original dataset includes 1700 case histories. In addition, the dataset augmentation procedure was used. As a result, the overall accuracy exceeded 84%. Furthermore, optimizing the network architecture and dataset has increased the overall accuracy by 17% and precision by 7%.

ГБОУ ВПО Красноярский государственный медицинский университет имени проф. В. Ф. Войно-Ясенецкого Министерства здравоохранения РФ, ректор-д. м. н., проф. И. П. Артюхов; кафедра внутренних болезней № 1, зав.-д. м. н., проф. С. Ю. Никулина. p%'>,%. В обзоре представлены литературные данные, свидетельствующие о генетической детерминированности ревматоидного артрита. Дано определение указанной патологии, описаны основные признаки заболевания, рассмотрены гены, влияющие на развитие ревматоидного артрита и их полиморфизмы. j+>7%";% 1+." : ревматоидный артрит, гены ревматоидного артрита.

Russian Society of Cardiology (RSC)With the participation: Eurasian Association of Therapists (EUAT), Society of Specialists in Heart Failure (OSSN), Russian Scientific Medical Society of Therapists (RNMOT), Russian Society of Pathologists, Russian Society of Radiologists and Radiologists (RSR)Endorsed by: Research and Practical Council of the Ministry of Health of the Russian Federation

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