Fetal Health Classification Using One-Dimensional Convolutional Neural Network

Anton Johan Röscher; Dustin van der Haar

doi:10.5220/0012322300003654

Fetal Health Classification Using One-Dimensional Convolutional Neural Network

Anton Johan Röscher
, Dustin van der Haar

Academy of Computer Science and Software Engineering

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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%.

Original language	English
Title of host publication	Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods
Editors	Modesto Castrillon-Santana, Maria De Marsico, Ana Fred
Publisher	Science and Technology Publications, Lda
Pages	671-678
Number of pages	8
ISBN (Print)	9789897586842
DOIs	https://doi.org/10.5220/0012322300003654
Publication status	Published - 2024
Event	13th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2024 - Rome, Italy Duration: 24 Feb 2024 → 26 Feb 2024

Publication series

Name	International Conference on Pattern Recognition Applications and Methods
Volume	1
ISSN (Electronic)	2184-4313

Conference

Conference	13th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2024
Country/Territory	Italy
City	Rome
Period	24/02/24 → 26/02/24

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

1D-CNN
CTG
Deep Learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Vision and Pattern Recognition

Access to Document

10.5220/0012322300003654

Cite this

Röscher, A. J., & van der Haar, D. (2024). Fetal Health Classification Using One-Dimensional Convolutional Neural Network. In M. Castrillon-Santana, M. De Marsico, & A. Fred (Eds.), Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods (pp. 671-678). (International Conference on Pattern Recognition Applications and Methods; Vol. 1). Science and Technology Publications, Lda. https://doi.org/10.5220/0012322300003654

Röscher, Anton Johan ; van der Haar, Dustin. / Fetal Health Classification Using One-Dimensional Convolutional Neural Network. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. editor / Modesto Castrillon-Santana ; Maria De Marsico ; Ana Fred. Science and Technology Publications, Lda, 2024. pp. 671-678 (International Conference on Pattern Recognition Applications and Methods).

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title = "Fetal Health Classification Using One-Dimensional Convolutional Neural Network",

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\%.",

keywords = "1D-CNN, CTG, Deep Learning",

author = "R{\"o}scher, \{Anton Johan\} and \{van der Haar\}, Dustin",

note = "Publisher Copyright: {\textcopyright} 2024 by SCITEPRESS – Science and Technology Publications, Lda.; 13th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2024 ; Conference date: 24-02-2024 Through 26-02-2024",

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isbn = "9789897586842",

series = "International Conference on Pattern Recognition Applications and Methods",

publisher = "Science and Technology Publications, Lda",

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booktitle = "Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods",

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Röscher, AJ & van der Haar, D 2024, Fetal Health Classification Using One-Dimensional Convolutional Neural Network. in M Castrillon-Santana, M De Marsico & A Fred (eds), Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. International Conference on Pattern Recognition Applications and Methods, vol. 1, Science and Technology Publications, Lda, pp. 671-678, 13th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2024, Rome, Italy, 24/02/24. https://doi.org/10.5220/0012322300003654

Fetal Health Classification Using One-Dimensional Convolutional Neural Network. / Röscher, Anton Johan; van der Haar, Dustin.
Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. ed. / Modesto Castrillon-Santana; Maria De Marsico; Ana Fred. Science and Technology Publications, Lda, 2024. p. 671-678 (International Conference on Pattern Recognition Applications and Methods; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fetal Health Classification Using One-Dimensional Convolutional Neural Network

AU - Röscher, Anton Johan

AU - van der Haar, Dustin

PY - 2024

Y1 - 2024

N2 - 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%.

AB - 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%.

KW - 1D-CNN

KW - CTG

KW - Deep Learning

UR - https://www.scopus.com/pages/publications/85190704037

U2 - 10.5220/0012322300003654

DO - 10.5220/0012322300003654

M3 - Conference contribution

AN - SCOPUS:85190704037

SN - 9789897586842

T3 - International Conference on Pattern Recognition Applications and Methods

SP - 671

EP - 678

BT - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods

A2 - Castrillon-Santana, Modesto

A2 - De Marsico, Maria

A2 - Fred, Ana

PB - Science and Technology Publications, Lda

T2 - 13th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2024

Y2 - 24 February 2024 through 26 February 2024

ER -

Röscher AJ, van der Haar D. Fetal Health Classification Using One-Dimensional Convolutional Neural Network. In Castrillon-Santana M, De Marsico M, Fred A, editors, Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods. Science and Technology Publications, Lda. 2024. p. 671-678. (International Conference on Pattern Recognition Applications and Methods). doi: 10.5220/0012322300003654

Fetal Health Classification Using One-Dimensional Convolutional Neural Network

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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