Version 1
: Received: 27 March 2024 / Approved: 28 March 2024 / Online: 28 March 2024 (13:34:04 CET)
How to cite:
Holcroft, S.; Karangwa, I.; Little, F.; Behoor, J.; Bazirete, O. Predictive Modeling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models. Preprints2024, 2024031769. https://doi.org/10.20944/preprints202403.1769.v1
Holcroft, S.; Karangwa, I.; Little, F.; Behoor, J.; Bazirete, O. Predictive Modeling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models. Preprints 2024, 2024031769. https://doi.org/10.20944/preprints202403.1769.v1
Holcroft, S.; Karangwa, I.; Little, F.; Behoor, J.; Bazirete, O. Predictive Modeling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models. Preprints2024, 2024031769. https://doi.org/10.20944/preprints202403.1769.v1
APA Style
Holcroft, S., Karangwa, I., Little, F., Behoor, J., & Bazirete, O. (2024). Predictive Modeling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models. Preprints. https://doi.org/10.20944/preprints202403.1769.v1
Chicago/Turabian Style
Holcroft, S., Joelle Behoor and Oliva Bazirete. 2024 "Predictive Modeling of Postpartum Haemorrhage Using Early Risk Factors: A Comparative Analysis of Statistical and Machine Learning Models" Preprints. https://doi.org/10.20944/preprints202403.1769.v1
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 re-gression with elastic-net regularisation, Random Forest, Extremely Randomized Trees, and gradient boosted trees with XGBoost. Random Forest emerged as the best predictive model for PPH across three different train-test data partitions. The important predictors identified in the 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 pre-diction 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
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
