Non-elective caesarean section risk assessment using Machine Learning techniques = Evaluación del riesgo de cesárea no electiva mediante técnicas de Machine Learning Articles uri icon

publication date

  • June 2024

start page

  • 1

end page

  • 10

issue

  • 3

volume

  • 51

International Standard Serial Number (ISSN)

  • 0210-573X

abstract

  • IBackground
    The sustained increase and the disparities in the rate of caesarean deliveries (CS) pose a public health challenge. Optimising the use of CS is of global concern and a challenge for public health. Machine Learning (ML) techniques can assist clinicians in decision-making, improving treatment modalities and patient outcomes.

    Methods
    Original data correspond to the 41,037 deliveries that took place at La Paz University Maternity Hospital (Madrid, Spain) between 2010 and 2018. Machine Learning (ML) model algorithm Random Forest (RF) was performed to determine the risk of CS. The first analysis was Mean Decrease Accuracy carried out on 50 permutations. The second analysis was ceteris-paribus and partial-dependence profiles.

    Results
    The RF models obtained identify a higher risk of CS delivery in multiple gestations, macrosomic foetuses and in those with prolonged gestation associated with other maternal–foetal characteristics. Results deny the consideration that older nulliparous mothers represent a specific obstetrtic risk goup.

    Conclusions
    ML techniques can be very useful in identifying risk factors to be addressed to optimise the number of CS. Macrosomia prevention programmes, reduction in the rate of multiple pregnancies or finishing pregnancy before the onset of risks associated with prolonged pregnancy may be ways to optimise the number of CS.

subjects

  • Computer Science
  • Medicine

keywords

  • maternal profile; multiple gestations; macrosomia; post-term gestations; obstetric interventionism