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Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models

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  • Shannon Holcroft

    (Department of Statistical Sciences, University of Cape Town, Cape Town 7701, South Africa)

  • Innocent Karangwa

    (Department of Statistical Sciences, University of Cape Town, Cape Town 7701, South Africa)

  • Francesca Little

    (Department of Statistical Sciences, University of Cape Town, Cape Town 7701, South Africa)

  • Joelle Behoor

    (Department of Statistical Sciences, University of Cape Town, Cape Town 7701, South Africa)

  • Oliva Bazirete

    (College of Medicine and Health Sciences, University of Rwanda, Kigali 3296, Rwanda)

Abstract

Postpartum haemorrhage (PPH) is a significant cause of maternal morbidity and mortality worldwide, particularly in low-resource settings. This study aimed to develop a predictive model for PPH using early risk factors and rank their importance in terms of predictive ability. The dataset was obtained from an observational case–control study in northern Rwanda. Various statistical models and machine learning techniques were evaluated, including logistic regression, logistic regression with elastic-net regularisation, Random Forests, Extremely Randomised Trees, and gradient-boosted trees with XGBoost. The Random Forest model, with an average sensitivity of 80.7%, specificity of 71.3%, and a misclassification rate of 12.19%, outperformed the other models, demonstrating its potential as a reliable tool for predicting PPH. The important predictors identified in this study were haemoglobin level during labour and maternal age. However, there were differences in PPH risk factor importance in different data partitions, highlighting the need for further investigation. These findings contribute to understanding PPH risk factors, highlight the importance of considering different data partitions and implementing cross-validation in predictive modelling, and emphasise the value of identifying the appropriate prediction model for the application. Effective PPH prediction models are essential for improving maternal health outcomes on a global scale. This study provides valuable insights for healthcare providers to develop predictive models for PPH to identify high-risk women and implement targeted interventions.

Suggested Citation

  • Shannon Holcroft & Innocent Karangwa & Francesca Little & Joelle Behoor & Oliva Bazirete, 2024. "Predictive Modelling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models," IJERPH, MDPI, vol. 21(5), pages 1-13, May.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:5:p:600-:d:1389824
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    References listed on IDEAS

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    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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