Enhancing Postpartum Haemorrhage Prediction Through the Integration of Classical Logistic Regression and Machine Learning Algorithms

Muriel Lérias-Cambeiro (), Raquel Mugeiro-Silva, Anabela Rodrigues, Tiago Dias-Domingues, Filipa Lança and António Vaz Carneiro
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Muriel Lérias-Cambeiro: Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal
Raquel Mugeiro-Silva: Department of Mathematical Sciences, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
Anabela Rodrigues: Department of Transfusion Medicine, Santa Maria University Hospital, 1649-028 Lisbon, Portugal
Tiago Dias-Domingues: Department of Mathematical Sciences, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
Filipa Lança: Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal
António Vaz Carneiro: Institute for Evidence Based Healthcare (ISBE), University of Lisbon, 1649-028 Lisbon, Portugal

Mathematics, 2025, vol. 13, issue 21, 1-23

Abstract: Postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. The early identification of bleeding risk in individual women is crucial for enabling timely interventions and improving patient outcomes.This study aims to evaluate various exploratory and classification methodologies, alongside optimisation strategies, for identifying predictors of postpartum haemorrhage. K-means clustering was employed on a retrospective cohort of patients, incorporating demographic, obstetric, and laboratory variables, to delineate patient profiles and select pertinent features. Initially, a classical logistic regression model, implemented without cross-validation, facilitated the identification of six significant predictors for postpartum haemorrhage: lactate dehydrogenase, urea, platelet count, non-O blood group, gestational age, and first-degree lacerations, all of which are variables routinely available in clinical practice. Furthermore, machine learning algorithms—including stepwise logistic regression, ridge logistic regression, and random forest—were utilised, applying cross-validation to optimise predictive performance and enhance generalisability. Among these methodologies, ridge logistic regression emerged as the most effective model, achieving the following metrics: sensitivity 0.857, specificity 0.875, accuracy 0.871, F1-score 0.759, and AUC 0.907. While machine learning techniques demonstrated superior performance, the integration of classical statistical methods with machine learning approaches provides a robust framework for generating reliable predictions and fostering significant clinical insights.

Keywords: clustering analysis; logistic regression; ridge logistic regression; random forest; machine learning models; postpartum haemorrhage; risk factors (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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