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

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

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.

Язык оригиналаанглийский
Название основной публикацииSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы758-761
Число страниц4
ISBN (электронное издание)9781728144016
ISBN (печатное издание)978-1-7281-4402-3
DOI
СостояниеОпубликовано - окт 2019
Событие2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Российская Федерация
Продолжительность: 21 окт 201927 окт 2019

Серия публикаций

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

Конференция

Конференция2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
СтранаРоссийская Федерация
ГородNovosibirsk
Период21.10.201927.10.2019

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    Ракитский, А. А., & Бочкарёв, Б. (2019). The study of the applicability of machine learning methods based on decision trees for holter monitoring. В SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings (стр. 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