Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees

Shahab Pasha, Jan Lundgren, Marco Carratù, Patrik Wreeby, Consolatina Liguori

2020

Abstract

This paper describes an artificial-intelligence–assisted screening system implemented to support medical cardiovascular examinations performed by doctors. The proposed system is a two-stage supervised classifier comprising a convolutional neural network for heart murmur detection and a decision tree for classifying vital signs. The classifiers are trained to prioritize higher-risk individuals for more time-efficient assessment. A feature selection approach is applied to maximize classification accuracy by using only the most significant vital signs correlated with heart issues. The results suggest that the trained convolutional neural network can learn and detect heart sound anomalies from the time-domain and frequency-domain signals without using any user-guided mathematical or statistical features. It is also concluded that the proposed two-stage approach improves diagnostic reliability and efficiency.

Paper Citation

in Harvard Style

Pasha S., Lundgren J., Carratù M., Wreeby P. and Liguori C. (2020). Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS; ISBN 978-989-758-398-8, SciTePress, pages 199-205. DOI: 10.5220/0008941801990205

in Bibtex Style

@conference{biosignals20,
author={Shahab Pasha and Jan Lundgren and Marco Carratù and Patrik Wreeby and Consolatina Liguori},
title={Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 4: BIOSIGNALS},
year={2020},
pages={199-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008941801990205},
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 (BIOSTEC 2020) - Volume 4: BIOSIGNALS
TI - Two-stage Artificial Intelligence Clinical Decision Support System for Cardiovascular Assessment using Convolutional Neural Networks and Decision Trees
SN - 978-989-758-398-8
AU - Pasha S.
AU - Lundgren J.
AU - Carratù M.
AU - Wreeby P.
AU - Liguori C.
PY - 2020
SP - 199
EP - 205
DO - 10.5220/0008941801990205
PB - SciTePress