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Fetal Health Prediction From Cardiotocography Recordings Using Kolmogorov-Arnold Networks.

W K Wong1, Filbert H Juwono2, Catur Apriono3

  • 1Department of Electrical and Computer EngineeringCurtin University Malaysia Miri 98009 Malaysia.

IEEE Open Journal of Engineering in Medicine and Biology
|July 14, 2025
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Summary

Kolmogorov-Arnold Networks (KANs) offer a novel approach to analyzing cardiotocograph (CTG) data for fetal health monitoring. This machine learning model significantly improves the accuracy of predicting fetal health conditions from CTG recordings.

Keywords:
CTGKANdeep learningfetal health

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Area of Science:

  • Medical Technology
  • Artificial Intelligence
  • Biomedical Signal Processing

Background:

  • Cardiotocograph (CTG) is crucial for monitoring fetal well-being during labor.
  • Interpreting CTG data is challenging due to its complex, nonlinear nature.
  • Accurate fetal health assessment is vital for timely interventions.

Purpose of the Study:

  • To develop a reliable machine learning model for predicting fetal health from CTG recordings.
  • To evaluate the efficacy of Kolmogorov-Arnold Networks (KANs) for this task.
  • To improve the accuracy and reliability of automated fetal health assessment.

Main Methods:

  • Utilized Kolmogorov-Arnold Networks (KANs), a novel neural network architecture.
  • Applied KANs to statistical features extracted from CTG recordings.
  • Validated the model on a publicly available dataset with labeled fetal health conditions.

Main Results:

  • KANs demonstrated superior performance compared to traditional machine learning models.
  • Achieved average classification accuracies of 93.6% for two-class and 92.6% for three-class tasks.
  • Highlighted the effectiveness of KANs in capturing nonlinear patterns in CTG data.

Conclusions:

  • The proposed KAN model shows significant promise for automated fetal health assessment.
  • KANs are effective in handling the inherent nonlinearity of CTG data.
  • This approach can enhance the accuracy and reliability of fetal health monitoring during labor.