Predicting delayed antenatal care initiation among pregnant women in East Africa: using machine learning algorithms

The prevalence of late antenatal care (ANC) initiation is high (65%). Promote family planning utilization, targeted health literacy campaigns, youth-friendly programs, expand mobile clinics, and promote skilled birth attendance to increase timely ANC initiation are recommended.

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Publication Details

Journal
Frontiers in Global Women s Health
Published
2025-10-03
DOI
https://doi.org/10.3389/fgwh.2025.1488391
Citations
1
Primary Topic
Global Maternal and Child Health
Type
article
Field-Weighted Citation Impact
3.49
Controls
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article

Predicting delayed antenatal care initiation among pregnant women in East Africa: using machine learning algorithms

Mequannent Sharew Melaku, Abriham Zegeye, Tirualem Zeleke Yehuala, Makda Fekadie Tewelgne et al.
1 citations
Frontiers in Global Women s Health
Global Maternal and Child Health
3.49
article

Predicting delayed antenatal care initiation among pregnant women in East Africa: using machine learning algorithms

Mequannent Sharew Melaku, Abriham Zegeye, Tirualem Zeleke Yehuala, Makda Fekadie Tewelgne, Fetlework Gubena Arage, Nebebe Demis Baykemagn, Eliyas Addisu Taye
article en
1 citations

Abstract

The prevalence of late antenatal care (ANC) initiation is high (65%). Promote family planning utilization, targeted health literacy campaigns, youth-friendly programs, expand mobile clinics, and promote skilled birth attendance to increase timely ANC initiation are recommended.

Frontiers in Global Women s HealthVol. 6
University of Gondar (ET)
Openalex Percentile: Top 7%
Global Maternal and Child Health
3.49
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