Open AccessSelection of neural network architecture and data augmentation procedures for predicting the course of cardiovascular diseases
Author(s) -
Mikhail Dorrer,
С. Е. Головенкин,
S. Yu. Nikulina,
Yu. V. Orlova,
E Yu Pelipeckaya,
T. D. Vereshchagina
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2094/3/032037
Subject(s) - artificial neural network , selection (genetic algorithm) , computer science , machine learning , artificial intelligence , architecture , course (navigation) , network architecture , data mining , engineering , aerospace engineering , art , computer security , visual arts
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%.