Fetal Health Classification Using One-Dimensional Convolutional Neural Network

Anton Röscher, Dustin van der Haar

2024

Abstract

Within the medical field, machine learning has the potential to allow doctors and medical professionals to make faster, more accurate diagnoses, empowering specialists to take immediate action. Early diagnosis and prevention of fetal health conditions can be achieved based on the biomarker data derived from the cardiotocography signals. The study proposes using a one-dimensional convolutional neural network for fetal health classification and compares it to conventional machine learning algorithms. A one-dimensional convolutional neural network is shown to outperform traditional machine learning algorithms in both data sets (CTU-CHB and UCI), with an accuracy of 89% - 94%.

Paper Citation

in Harvard Style

Röscher A. and van der Haar D. (2024). Fetal Health Classification Using One-Dimensional Convolutional Neural Network. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 671-678. DOI: 10.5220/0012322300003654

in Bibtex Style

@conference{icpram24,
author={Anton Röscher and Dustin van der Haar},
title={Fetal Health Classification Using One-Dimensional Convolutional Neural Network},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={671-678},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012322300003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Fetal Health Classification Using One-Dimensional Convolutional Neural Network
SN - 978-989-758-684-2
AU - Röscher A.
AU - van der Haar D.
PY - 2024
SP - 671
EP - 678
DO - 10.5220/0012322300003654
PB - SciTePress