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Authors: Anton Dolganov and Vladimir Kublanov

Affiliation: Ural Federal University, Russian Federation

Keyword(s): Decision Support, Machine Learning, Feature Selection, Arterial Hypertension, Heart Rate Variability.

Abstract: The study describes a preliminary stage of the decision support system development for cardiovascular system disorders. As the clinical model of the disorders, the arterial hypertension was used. The study consisted of two steps: diagnosing of the arterial hypertension and an evaluation of the treatment efficiency during the neuro-electrostimulation application. For the diagnosing part, a clinical study was conducted involving heart rate variability signals recording while performing tilt-test functional load. Performance of different machine learning techniques and feature selection strategies in task of binary classification (healthy volunteers and patients suffering from arterial hypertension) were compared. The genetic programming feature selection and quadratic discriminant analysis classifier reached the highest classification accuracy. Best feature combinations were used to evaluate a treatment efficiency. The results indicate the potential of the proposed decision support sys tem. (More)

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Paper citation in several formats:
Dolganov, A. and Kublanov, V. (2018). Towards a Decision Support System for Disorders of the Cardiovascular System - Diagnosing and Evaluation of the Treatment Efficiency. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - AI4Health; ISBN 978-989-758-281-3; ISSN 2184-4305, SciTePress, pages 727-733. DOI: 10.5220/0006753407270733

@conference{ai4health18,
author={Anton Dolganov. and Vladimir Kublanov.},
title={Towards a Decision Support System for Disorders of the Cardiovascular System - Diagnosing and Evaluation of the Treatment Efficiency},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - AI4Health},
year={2018},
pages={727-733},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006753407270733},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - AI4Health
TI - Towards a Decision Support System for Disorders of the Cardiovascular System - Diagnosing and Evaluation of the Treatment Efficiency
SN - 978-989-758-281-3
IS - 2184-4305
AU - Dolganov, A.
AU - Kublanov, V.
PY - 2018
SP - 727
EP - 733
DO - 10.5220/0006753407270733
PB - SciTePress

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