Selection of neural network architecture and data augmentation procedures for predicting the course of cardiovascular diseases

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...

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Bibliographic Details
Published in:Journal of physics. Conference series 2021-11, Vol.2094 (3), p.32037
Main Authors: Dorrer, M G, Golovenkin, S E, Nikulina, S Yu, Orlova, Yu V, Pelipeckaya, E Yu, Vereshchagina, T D
Format: Article
Language:English
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Summary: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%.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2094/3/032037