Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population

Abstract

Introduction: Postpartum hemorrhage (PPH) is a significant cause of maternal mortality worldwide, particularly in low- and middle-income countries. It is essential to develop effective prediction models to identify women at risk of PPH and implement appropriate interventions to reduce maternal morbidity and mortality. This study aims to predict the occurrence of postpartum hemorrhage using machine learning models based on antenatal, intrapartum, and postnatal visit data obtained from the Kenya Antenatal and Postnatal Care Research Collective cohort.

Method: Four machine learning models - logistic regression, naïve Bayes, decision tree, and random forest - were constructed using 67% training data (1,056/1,576). The training data was further split into 67% for model building and 33% cross validation. Once the models are built, the remaining 33% (520/1,576) independent test data was used for external validation to confirm the models' performance. Models were fine-tuned using feature selection through extra tree classifier technique. Model performance was assessed using accuracy, sensitivity, and area under the curve (AUC) of the receiver operating characteristics (ROC) curve.

Result: The naïve Bayes model performed best with 0.95 accuracy, 0.97 specificity, and 0.76 AUC. Seven factors (anemia, limited prenatal care, hemoglobin concentrations, signs of pallor at intrapartum, intrapartum systolic blood pressure, intrapartum diastolic blood pressure, and intrapartum respiratory rate) were associated with PPH prediction in Kenyan population.

Discussion: This study demonstrates the potential of machine learning models in predicting PPH in the Kenyan population. Future studies with larger datasets and more PPH cases should be conducted to improve prediction performance of machine learning model. Such prediction algorithms would immensely help to construct a personalized obstetric path for each pregnant patient, improve resource allocation, and reduce maternal mortality and morbidity.

Keywords: LMICs; machine learning; maternal health; postpartum hemorrhage; pregnancy; risk prediction.

Figure 1

Flow chart of Kenya maternal cohort.

Figure 2

Receiver operating characteristics curves of four machine learning model of PPH prediction (AUC, area under the curve; GaussianNB, naïve Bayes).

Figure 3

SHAP summary plot of top 7 features for naïve Bayes PPH prediction model. Color represents relative feature value for each patient. Blue color indicates low feature value whereas red color indicates high feature value. Positive SHAP values suggest greater PPH risk and negative SHAP values are protective for PPH (Hb, hemoglobin; ANC, antenatal clinic; SBP, systolic blood pressure; DBP, diastolic blood pressure; BPM, breaths per minute, SHAP, Shapley Additive exPlanations).