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.
Authors
- Gebremeskel Kibret Abebe (ORCID: https://orcid.org/0000-0001-8985-6291)
- Agmasie Damtew Walle (ORCID: https://orcid.org/0000-0001-9583-5876)
- Tilahun Dessie Alene
- Habtamu Setegn Ngusie (ORCID: https://orcid.org/0000-0003-4666-1114)
- Ermias Bekele Enyew (ORCID: https://orcid.org/0000-0002-9310-2481)
- Getanew Aschalew Tesfa (ORCID: https://orcid.org/0000-0003-2115-1337)
- Alemu Birara Zemariam (ORCID: https://orcid.org/0000-0001-8195-3011)
- Asefa Adimasu Taddese
Institutions
- Debre Berhan University (ET)
- Hong Kong Baptist University (HK)
- Wollo University (ET)
- Dilla University (ET)
- Woldia University (ET)
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