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

Elin Haerani; Fadhilah Syafria; Novriyanto Novriyanto; Ismail Marzuki

doi:10.37385/3n6z5n26

Authors

Elin Haerani Universitas Islam Negeri Sultan Syarif Kasim Riau
Fadhilah Syafria Universitas Islam Negeri Sultan Syarif Kasim Riau
Novriyanto Novriyanto Universitas Islam Negeri Sultan Syarif Kasim Riau
Ismail Marzuki InterDigital (Canada)

DOI:

https://doi.org/10.37385/3n6z5n26

Keywords:

Cardiotocography (CTG), Backpropagation Neural Network (BPNN), SMOTE, Classification, Early Foetal Detection, Health AI

Abstract

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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