AI Medical Compendium Journal:
BMC pregnancy and childbirth

Showing 51 to 54 of 54 articles

Estimation of umbilical cord blood leptin and insulin based on anthropometric data by means of artificial neural network approach: identifying key maternal and neonatal factors.

BMC pregnancy and childbirth
BACKGROUND: Leptin and insulin levels are key factors regulating fetal and neonatal energy homeostasis, development and growth. Both biomarkers are used as predictors of weight gain and obesity during infancy. There are currently no prediction algori...

Exploring machine learning algorithms to predict short birth intervals and identify its determinants among reproductive-age women in East Africa.

BMC pregnancy and childbirth
BACKGROUND: The occurrence of short birth intervals among reproductive-age women in East Africa is a critical public health issue, contributing to maternal and child health risks. Identifying the key factors that predict short birth intervals can hel...

Application of machine learning in identifying risk factors for low APGAR scores.

BMC pregnancy and childbirth
BACKGROUND: Identifying the risk factors for low APGAR scores at birth is critical for improving neonatal outcomes and guiding clinical interventions.

Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.

BMC pregnancy and childbirth
BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combine...