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Efficacy of Machine Learning in Predicting the Kind of Delivery by Cardiotocography

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XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019 (MEDICON 2019)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 76))

Abstract

It is well known that the interpretation of cardiotocographic (CTG) signals is still subjective and prone to misinterpretations; as a consequence, there has been an increase of cesarean sections, often not necessary, and initial expectations of significantly reducing perinatal morbidity and mortality have been unattended. Nevertheless, in developed countries, CTG is still the most widely employed prenatal technique for monitoring fetal health; and in many countries it represents a medical report with legal value. To overcome the drawbacks of the visual interpretation, many computerized systems for automatic or semi-automatic analysis of CTG have been developed in the last years. Recently, in order to support the diagnosis process, and to increase the predictive capability of these systems, also other techniques such as artificial neural network, deep learning and machine learning have been introduced. In previous works of the authors, software for automatic CTG analysis has been developed and described in detail. Now, by employing a dataset of features extracted from CTG signals with that software, to enhance its performances, different algorithms, such as J48, Adaboosting, Random Forests and Gradient Boosted Tree, have been tested to predict whether a birth would be a caesarean section or a vaginal delivery. The RF algorithm showed the best performance, since it reached the highest accuracy (87.6%), precision (87.9%) and AUCROC (93.0%). These preliminary results are very satisfying and encouraging; they confirm that to enrich the CTG analysis software with this methodology can help to significantly improve CTG classification.

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Author information

Authors and Affiliations

  1. Department of Public Health, University Hospital of Naples Federico II, Naples, Italy

    Giovanni Improta

  2. Department of Advanced Biomedical Sciences, University Hospital of Naples Federico II, Naples, Italy

    Carlo Ricciardi

  3. Department of Electrical Engineering and Information Technology, DIETI, University of Naples Federico II, Naples, Italy

    Francesco Amato

  4. Istituti Clinici Scientifici Maugeri IRCCS, Telese Terme, BN, Italy

    Giovanni D’Addio, Mario Cesarelli & Maria Romano

  5. Department of Experimental and Clinical Medicine (DMSC), University “Magna Graecia” of Catanzaro, Catanzaro, Italy

    Maria Romano

Authors
  1. Giovanni Improta
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  2. Carlo Ricciardi
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  3. Francesco Amato
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  4. Giovanni D’Addio
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  5. Mario Cesarelli
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  6. Maria Romano
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Corresponding author

Correspondence to Giovanni Improta .

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Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Jorge Henriques

  2. Universidade do Minho, Braga, Portugal

    Nuno Neves

  3. University of Coimbra, Coimbra, Portugal

    Paulo de Carvalho

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Improta, G., Ricciardi, C., Amato, F., D’Addio, G., Cesarelli, M., Romano, M. (2020). Efficacy of Machine Learning in Predicting the Kind of Delivery by Cardiotocography. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_95

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  • DOI: https://doi.org/10.1007/978-3-030-31635-8_95

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