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

The study shows that machine learning can classify ANC quality and reveal spatial disparities, aiding policymakers in targeting programs and allocating resources.

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Publication Details

Journal
PLoS ONE
Published
2025-11-26
DOI
https://doi.org/10.1371/journal.pone.0337643
Primary Topic
Global Maternal and Child Health
Type
article
Field-Weighted Citation Impact
0.00
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article

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

S. Chakraborty
PLoS ONE
Global Maternal and Child Health
article

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

S. Chakraborty
article en

Abstract

The study shows that machine learning can classify ANC quality and reveal spatial disparities, aiding policymakers in targeting programs and allocating resources.

PLoS ONEVol. 20(11)
University of Chittagong (BD)
Openalex Percentile: Top 53%
Global Maternal and Child Health
DOIOpen Access PDFOpenAlex
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Machine learning meets maternal health: Uncovering spatial blind spots in antenatal care quality in Bangladesh — S. Chakraborty · PLoS ONE (2025) | TGRS Research Map | TGRS