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

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. 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. 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. 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.

Informations de copyright

© 2023 Shah, Saxena, Rani, Nelaturi, Gill, Tippett Barr, Were, Khagayi, Ouma, Akelo, Norwitz, Ramakrishnan, Onyango and Teltumbade.

Déclaration de conflit d'intérêts

SS, SR and NN are employees of CognitiveCare Inc.'s wholly owned subsidiary. SYS, SG and MT are founding team members and employees of CognitiveCare Inc. CognitiveCare Inc. has a patent pending for a maternal and infant health intelligence and cognitive insight (MIHIC) system and score to predict the risk of maternal, fetal and infant morbidity and mortality. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.