Machine Learning Possibilities for Evaluation of Arterial Hypertension Treatment Efficiency in Case Study

Vladimir S. Kublanov, Yan E. Kazakov, Anton Yu. Dolganov

2020

Abstract

The paper aims to discuss questions concerning application of the machine learning based decisions in the area of the clinical diagnostics. In previous works it was shown that it is possible to develop a decision support system based on the most indicative parameters of the short-term heart rate variability signals for the express diagnosing of the arterial hypertension using methods of machine learning. This paper show results of the case-study for analysis of the machine learning based results for evaluating duration of the treatment using the device for the neuro-electrostimulation. Comparative analysis of the results of the quadratic discriminant analysis application and instrumental measurements highlights concern regarding using of a single method in such complex task as a clinical process. Possible limitations and advantages of each method were discussed.

Paper Citation

in Harvard Style

Kublanov V., Kazakov Y. and Dolganov A. (2020). Machine Learning Possibilities for Evaluation of Arterial Hypertension Treatment Efficiency in Case Study. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: NDNSNT, ISBN 978-989-758-398-8, pages 411-416. DOI: 10.5220/0009372004110416

in Bibtex Style

@conference{ndnsnt20,
author={Vladimir Kublanov and Yan Kazakov and Anton Dolganov},
title={Machine Learning Possibilities for Evaluation of Arterial Hypertension Treatment Efficiency in Case Study},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: NDNSNT,},
year={2020},
pages={411-416},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009372004110416},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: NDNSNT,
TI - Machine Learning Possibilities for Evaluation of Arterial Hypertension Treatment Efficiency in Case Study
SN - 978-989-758-398-8
AU - Kublanov V.
AU - Kazakov Y.
AU - Dolganov A.
PY - 2020
SP - 411
EP - 416
DO - 10.5220/0009372004110416