Exploring machine learning algorithms for predicting fertility preferences among reproductive age women in Nigeria
This study highlights the potential of machine learning for analyzing complex demographic data, revealing hidden factors associated with fertility preferences among Nigerian women. In conclusion, these findings can inform more effective family planning interventions, promoting sustainable development across Nigeria.
Authors
- Kusse Urmale Mare (ORCID: https://orcid.org/0000-0001-8522-1504)
- Teshome Demis Nimani (ORCID: https://orcid.org/0000-0002-8982-9362)
- Zinabu Bekele Tadese (ORCID: https://orcid.org/0009-0003-4655-2185)
- Fetlework Gubena
- Jamilu Sani (ORCID: https://orcid.org/0009-0001-9089-5973)
- Ismail Garba Wali
Institutions
- Haramaya University (ET)
- Federal University Birnin Kebbi (NG)
- Samara University (ET)
- University of Gondar (ET)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2025-01-16
- DOI
- https://doi.org/10.3389/fdgth.2024.1495382
- Citations
- 4
- Primary Topic
- Insurance, Mortality, Demography, Risk Management
- Type
- article
- Field-Weighted Citation Impact
- 22.37