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Early Detection of Foetal Pathological Conditions with Neural Network Method: Implementation of Backpropagation Neural Network and SMOTE on Cardiotocography Data

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 7 No. 2 (2026) · hlm. 1396–1406

DOI
10.37385/3n6z5n26
ISSN
2715-6079, 2715-6087
Terbit
2026-06-15
Penerbit
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Bahasa
en

Abstrak

This research focuses on the development of an effective classification model for early detection of foetal pathological conditions using Cardiotocography (CTG) data by utilising the Backpropagation Neural Network (BPNN) method. The high maternal mortality rate (MMR) and infant mortality rate (IMR) in Indonesia, including Riau Province, emphasise the importance of accurate prenatal diagnosis. The main challenge of this research is to address the class imbalance issue in the CTG dataset, which is biased towards the Normal class (77.9%) compared to the Suspect (13.9%) and Pathological (8.2%) classes. This problem was addressed by applying the Synthetic Minority Oversampling Technique (SMOTE). The model's performance was evaluated using K-Fold Cross Validation (5-Fold and 10-Fold). The test results showed that the combination of BPNN and SMOTE significantly improved performance, achieving a highest average accuracy of 92.66% and a maximum accuracy of 94.84% in the 10-Fold Cross Validation scheme. The resulting model is stable, has a high generalisation capability, and has great potential to be integrated into an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) to support evidence-based health policies in reducing Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR).

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10 penulis · 25 tautan kepenulisan bersama · 13 lintas organisasi

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  • Universitas Islam Negeri Sultan Syarif Kasim Riau
  • InterDigital (Canada)
  • Sheffield Hallam University
  • Riau University
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
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Rekan penulisOrganisasiArtikel bersama
Fitra Ayu LestariUniversitas Islam Negeri Sultan Syarif Kasim Riau4
Dwi Utari IswavigraUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Okfalisa OkfalisaUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Saktioto SaktiotoRiau University1
Hidayati RusnedyUniversitas Islam Negeri Sultan Syarif Kasim Riau1
Bernardi PranggonoSheffield Hallam University1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.yrpipku.com/index.php/jaets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en">Early Detection of Foetal Pathological Conditions with Neural Network Method: Implementation of Backpropagation Neural Network and SMOTE on Cardiotocography Data</dc:title>
	<dc:creator xml:lang="en">Haerani, Elin</dc:creator>
	<dc:creator xml:lang="en">Syafria, Fadhilah</dc:creator>
	<dc:creator xml:lang="en">Novriyanto, Novriyanto</dc:creator>
	<dc:creator xml:lang="en">Marzuki, Ismail</dc:creator>
	<dc:subject xml:lang="en">Cardiotocography (CTG)</dc:subject>
	<dc:subject xml:lang="en">Backpropagation Neural Network (BPNN)</dc:subject>
	<dc:subject xml:lang="en">SMOTE</dc:subject>
	<dc:subject xml:lang="en">Classification</dc:subject>
	<dc:subject xml:lang="en">Early Foetal Detection</dc:subject>
	<dc:subject xml:lang="en">Health AI</dc:subject>
	<dc:description xml:lang="en">This research focuses on the development of an effective classification model for early detection of foetal pathological conditions using Cardiotocography (CTG) data by utilising the Backpropagation Neural Network (BPNN) method. The high maternal mortality rate (MMR) and infant mortality rate (IMR) in Indonesia, including Riau Province, emphasise the importance of accurate prenatal diagnosis. The main challenge of this research is to address the class imbalance issue in the CTG dataset, which is biased towards the Normal class (77.9%) compared to the Suspect (13.9%) and Pathological (8.2%) classes. This problem was addressed by applying the Synthetic Minority Oversampling Technique (SMOTE). The model's performance was evaluated using K-Fold Cross Validation (5-Fold and 10-Fold). The test results showed that the combination of BPNN and SMOTE significantly improved performance, achieving a highest average accuracy of 92.66% and a maximum accuracy of 94.84% in the 10-Fold Cross Validation scheme. The resulting model is stable, has a high generalisation capability, and has great potential to be integrated into an Artificial Intelligence (AI)-based Clinical Decision Support System (CDSS) to support evidence-based health policies in reducing Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR).</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2026-06-15</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.yrpipku.com/index.php/jaets/article/view/10665</dc:identifier>
	<dc:identifier>10.37385/3n6z5n26</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS); 1396-1406</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v7i2</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/10665/6417</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Elin Haerani, Fadhilah Syafria, Novriyanto Novriyanto, Ismail Marzuki</dc:rights>
</oai_dc:dc>