Standard

The study of the applicability of machine learning methods based on decision trees for holter monitoring. / Ракитский, Антон Андреевич; Бочкарёв, Борис.

SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 758-761 8958257 (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).

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

Harvard

Ракитский, АА & Бочкарёв, Б 2019, The study of the applicability of machine learning methods based on decision trees for holter monitoring. in SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings., 8958257, SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 758-761, 2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019, Novosibirsk, Russian Federation, 21.10.2019. https://doi.org/10.1109/SIBIRCON48586.2019.8958257

APA

Ракитский, А. А., & Бочкарёв, Б. (2019). The study of the applicability of machine learning methods based on decision trees for holter monitoring. In SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings (pp. 758-761). [8958257] (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIBIRCON48586.2019.8958257

Vancouver

Ракитский АА, Бочкарёв Б. The study of the applicability of machine learning methods based on decision trees for holter monitoring. In SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 758-761. 8958257. (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings). doi: 10.1109/SIBIRCON48586.2019.8958257

Author

Ракитский, Антон Андреевич ; Бочкарёв, Борис. / The study of the applicability of machine learning methods based on decision trees for holter monitoring. SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 758-761 (SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings).

BibTeX

@inproceedings{753e48e498514ea9a3fd9f73275478e4,
title = "The study of the applicability of machine learning methods based on decision trees for holter monitoring",
abstract = "In this paper we investigate the possibility of using machine learning methods based on decision trees for the analysis of electrocardiograms. In present work we consider and investigate such methods as gradient boosting, random forest and extra trees because they are most suitable for solving same problems. The obtained results show us the high efficiency of the considered methods and prove the possibility of their use for automatization of the electrocardiograms analysis.",
keywords = "electrocardiography, extra trees, gradient boosting, Holter monitoring, machine learning, random forest",
author = "Ракитский, {Антон Андреевич} and Борис Бочкарёв",
year = "2019",
month = oct,
doi = "10.1109/SIBIRCON48586.2019.8958257",
language = "English",
isbn = "978-1-7281-4402-3",
series = "SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "758--761",
booktitle = "SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings",
address = "United States",
note = "2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 ; Conference date: 21-10-2019 Through 27-10-2019",

}

RIS

TY - GEN

T1 - The study of the applicability of machine learning methods based on decision trees for holter monitoring

AU - Ракитский, Антон Андреевич

AU - Бочкарёв, Борис

PY - 2019/10

Y1 - 2019/10

N2 - In this paper we investigate the possibility of using machine learning methods based on decision trees for the analysis of electrocardiograms. In present work we consider and investigate such methods as gradient boosting, random forest and extra trees because they are most suitable for solving same problems. The obtained results show us the high efficiency of the considered methods and prove the possibility of their use for automatization of the electrocardiograms analysis.

AB - In this paper we investigate the possibility of using machine learning methods based on decision trees for the analysis of electrocardiograms. In present work we consider and investigate such methods as gradient boosting, random forest and extra trees because they are most suitable for solving same problems. The obtained results show us the high efficiency of the considered methods and prove the possibility of their use for automatization of the electrocardiograms analysis.

KW - electrocardiography

KW - extra trees

KW - gradient boosting

KW - Holter monitoring

KW - machine learning

KW - random forest

UR - http://www.scopus.com/inward/record.url?scp=85079044839&partnerID=8YFLogxK

UR - https://elibrary.ru/item.asp?id=43253193

U2 - 10.1109/SIBIRCON48586.2019.8958257

DO - 10.1109/SIBIRCON48586.2019.8958257

M3 - Conference contribution

AN - SCOPUS:85079044839

SN - 978-1-7281-4402-3

T3 - SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

SP - 758

EP - 761

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019

Y2 - 21 October 2019 through 27 October 2019

ER -

ID: 23078619