Ensemble learning to predict short birth interval among reproductive-age women in Ethiopia: evidence from EDHS 2016–2019
Random forest was best predictive models with improved performance. "The most significant features that contribute to the accuracy of the top-performing models, notably the Random Forest should be highlighted because they outperformed the other model in the analysis.In general, ensemble learning algorithms can accurately predict short birth interval status, making them potentially useful as decision-support tools for the pertinent stakeholders.
Authors
- Wubshet Debebe Negash (ORCID: https://orcid.org/0000-0001-9720-7558)
- Hanna Demissie
- Jenberu Mekurianew Kelkay (ORCID: https://orcid.org/0009-0004-5526-3454)
- Deje Sendek Anteneh (ORCID: https://orcid.org/0009-0004-7059-8041)
- Abraham Dessie Gessesse (ORCID: https://orcid.org/0009-0002-8356-4460)
- Henok Dessie Wubneh
- Birku Getie Mihret
- Gebeyehu Fassil Gebeyehu
- Kalkidan Kassahun Aweke
- Mikiyas Birhanu Ejigu
- Mathias Amare Sendeku
- Kirubel Adrissie Barkneh
Institutions
- Australian National University (AU)
- Debark University (ET)
- Woldia University (ET)
- Bahir Dar University (ET)
- University of Gondar (ET)
Publication Details
- Journal
- BMC Pregnancy and Childbirth
- Published
- 2025-02-05
- DOI
- https://doi.org/10.1186/s12884-025-07248-1
- Citations
- 1
- Primary Topic
- Global Maternal and Child Health
- Type
- article
- Field-Weighted Citation Impact
- 3.49