The study of the applicability of machine learning methods based on decision trees for holter monitoring › Research Explorer

Novosibirsk State University's Research Portal

The study of the applicability of machine learning methods based on decision trees for holter monitoring

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

DOI

https://doi.org/10.1109/SIBIRCON48586.2019.8958257
Final published version

Антон Андреевич Ракитский
Борис Бочкарёв

Original language	English
Title of host publication	SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	758-761
Number of pages	4
ISBN (Electronic)	9781728144016
ISBN (Print)	978-1-7281-4402-3
DOIs	https://doi.org/10.1109/SIBIRCON48586.2019.8958257
Publication status	Published - Oct 2019
Event	2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Russian Federation Duration: 21 Oct 2019 → 27 Oct 2019

Publication series

Name	SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

Conference

Conference	2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
Country/Territory	Russian Federation
City	Novosibirsk
Period	21.10.2019 → 27.10.2019

OECD FOS+WOS

5.09 OTHER SOCIAL SCIENCES
1.01 MATHEMATICS
1.02 COMPUTER AND INFORMATION SCIENCES
1.03 PHYSICAL SCIENCES AND ASTRONOMY

Research areas

electrocardiography, extra trees, gradient boosting, Holter monitoring, machine learning, random forest

ID: 23078619