Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques

This study underscores the vital role of machine learning algorithms in predicting health facility deliveries. A slight decline in facility deliveries from previous reports highlights the urgent need for targeted interventions to meet Sustainable Development Goals (SDGs), particularly in maternal health. The study recommends promoting facility-based deliveries. These include raising awareness about skilled birth attendance, encouraging early ANC check-up, addressing financial barriers through targeted support programs, implementing culturally sensitive interventions, utilizing media campaigns, and mobile health initiatives. Design specific interventions tailored to the birth order of the child, recognizing that mothers may have different informational needs depending on whether it is their first or subsequent delivery. Furthermore, we recommended researchers to explore a variety of techniques and validate findings using more recent data.

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Publication Details

Journal
Frontiers in Public Health
Published
2024-11-27
DOI
https://doi.org/10.3389/fpubh.2024.1439320
Citations
3
Primary Topic
Global Maternal and Child Health
Type
article
Field-Weighted Citation Impact
3.33
Controls
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article

Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques

Gebremeskel Kibret Abebe, Agmasie Damtew Walle, Tilahun Dessie Alene, Habtamu Setegn Ngusie et al.
3 citations
Frontiers in Public Health
Global Maternal and Child Health
3.33
article

Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques

Gebremeskel Kibret Abebe, Agmasie Damtew Walle, Tilahun Dessie Alene, Habtamu Setegn Ngusie, Ermias Bekele Enyew, Getanew Aschalew Tesfa, Alemu Birara Zemariam, Asefa Adimasu Taddese
article en
3 citations

Abstract

This study underscores the vital role of machine learning algorithms in predicting health facility deliveries. A slight decline in facility deliveries from previous reports highlights the urgent need for targeted interventions to meet Sustainable Development Goals (SDGs), particularly in maternal health. The study recommends promoting facility-based deliveries. These include raising awareness about skilled birth attendance, encouraging early ANC check-up, addressing financial barriers through targeted support programs, implementing culturally sensitive interventions, utilizing media campaigns, and mobile health initiatives. Design specific interventions tailored to the birth order of the child, recognizing that mothers may have different informational needs depending on whether it is their first or subsequent delivery. Furthermore, we recommended researchers to explore a variety of techniques and validate findings using more recent data.

Frontiers in Public HealthVol. 12
Debre Berhan University (ET), Hong Kong Baptist University (HK), Wollo University (ET), Dilla University (ET), Woldia University (ET)
Good health and well-being
Openalex Percentile: Top 7%
Global Maternal and Child Health
3.33
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Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques — Gebremeskel Kibret Abebe, Agmasie Damtew Walle, et al. · Frontiers in Public Health (2024) | TGRS Research Map | TGRS