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

The study demonstrates that machine learning models can effectively identify key predictors of short birth intervals among reproductive-age women in East Africa, providing valuable insights for designing targeted public health interventions to improve maternal and child health outcomes in East Africa.

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Publication Details

Journal
BMC Pregnancy and Childbirth
Published
2025-05-09
DOI
https://doi.org/10.1186/s12884-025-07668-z
Citations
2
Primary Topic
Global Maternal and Child Health
Type
article
Field-Weighted Citation Impact
6.99
Controls
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article

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

Sisay Maru Wubante, Bezawit Melak Fente, Tirualem Zeleke Yehuala
2 citations
BMC Pregnancy and Childbirth
Global Maternal and Child Health
6.99
article

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

Sisay Maru Wubante, Bezawit Melak Fente, Tirualem Zeleke Yehuala
article en
2 citations

Abstract

The study demonstrates that machine learning models can effectively identify key predictors of short birth intervals among reproductive-age women in East Africa, providing valuable insights for designing targeted public health interventions to improve maternal and child health outcomes in East Africa.

BMC Pregnancy and ChildbirthVol. 25(1)
University of Gondar (ET)
Gender equality
Openalex Percentile: Top 4%
Global Maternal and Child Health
6.99
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