Machine learning meets maternal health: Uncovering spatial blind spots in antenatal care quality in Bangladesh.

ABSTRACT:

Background

High-quality antenatal care (ANC) is defined as four or more antenatal visits with at least one to a medically trained provider, measurement of weight and blood pressure, testing of blood and urine, and receipt of information on potential danger signs at least once during pregnancy. Though Bangladesh has almost universal ANC coverage, there is widespread inequality in the quality of these services. Traditional statistical models utilized in studies have tended to disregard complicated interconnections between socio-demographic, service-based, and regional factors that influence the quality of ANC. Using nationally representative data, this paper applies machine learning (ML) approaches to classify ANC quality, identify regional hotspots of low-quality care, and its facto

SUBMITTER: Chakraborty S 

PROVIDER: S-EPMC12654952 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature