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

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

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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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%.",

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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

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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%.

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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

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