Preeclampsia (PE) is a significant concern in pregnancy, contributing to approximately 20% of maternal deaths and 15% of preterm births globally, with around 8.5 million cases reported annually.

PPH is a major obstetric emergency and a leading cause of maternal mortality, highlighting the urgent need for improved prediction and management strategies.

A cohort study identified significant risk factors for PPH, including incomplete delivery of the placenta, labor progression failure, and maternal conditions like obesity and hypertension.

Research indicates that machine learning algorithms, particularly Naive Bayes, can significantly enhance the prediction and management of PPH, ultimately improving maternal health outcomes.

Machine learning has emerged as a promising tool for predicting PPH by analyzing large datasets to uncover patterns that traditional models may overlook.

This study evaluated the predictive accuracy of four machine learning algorithms—Naive Bayes, Decision Tree, Random Forest, and Support Vector Machine—for predicting PPH using clinical risk factors.

The final prediction model demonstrated the best performance with the Random Forest algorithm, achieving an error rate of 19.16% and an AUC_ROC value of 0.7390.

The study aims to develop risk prediction models for PE tailored to the Xinjiang population, utilizing clinical symptoms and placental growth factor (PlGF) levels.

In Xinjiang, China, the incidence of PE is notably high, reaching up to 9.1%, influenced by the region's diverse ethnic groups and unique lifestyles.

After implementing prediction models, the incidence of PE among hospitalized pregnant women in Xinjiang decreased significantly from 7.2% to 2.0%.

FTIR spectroscopy, combined with machine learning, shows potential for early diagnosis and better monitoring of pre-eclampsia, which could improve maternal and fetal outcomes.

Approximately 500,000 maternal deaths each year are attributed to pregnancy-related complications, with postpartum hemorrhage (PPH) being a critical factor affecting 1-5% of births worldwide.