The evaluation of functional heart condition with machine learning algorithms; Journal of Physics: Conference Series; Vol. 881 : Innovations in Non-Destructive Testing (SibTest 2017)

The evaluation of functional heart condition with machine learning algorithms; Journal of Physics: Conference Series; Vol. 881 : Innovations in Non-Destructive Testing (SibTest 2017)

Bibliographic Details
Parent link:Journal of Physics: Conference Series
Vol. 881 : Innovations in Non-Destructive Testing (SibTest 2017).— 2017.— [012009, 5 p.]
Corporate Author: Национальный исследовательский Томский политехнический университет (ТПУ) Институт неразрушающего контроля (ИНК) Кафедра промышленной и медицинской электроники (ПМЭ)
Other Authors: Overchuk K. V., Lezhnina I. A. Inna Alekseevna, Uvarov A. A. Aleksandr Andreevich, Perchatkin V. A., Lvova A. B.
Summary:Title screen
This paper is considering the most suitable algorithms to build a classifier for evaluating of the functional heart condition with the ability to estimate the direction and progress of the patient's treatment. The cons and pros of algorithms was analyzed with respect to the problem posed. The most optimal solution has been given and justified.
Режим доступа: по договору с организацией-держателем ресурса
Language:English
Published: 2017
Subjects:
электронный ресурс
труды учёных ТПУ
функциональное состояние
сердце
алгоритмы
машинное обучение
классификаторы
Online Access:http://dx.doi.org/10.1088/1742-6596/881/1/012009
http://earchive.tpu.ru/handle/11683/43878
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=656286

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