Application of the random forest algorithm to predict skilled birth attendance and identify determinants among reproductive-age women in 27 Sub-Saharan African countries; machine learning analysis
Dados Bibliográficos
| ID | 15381663 |
|---|---|
| Autores | Eliyas Addisu Taye (0009-0005-1561-072X, University of Gondar, autor correspondente), Eden Yitbarek Woubet (University of Gondar), Gabrela Yimer Hailie (University of Gondar), Fetlework Gubena Arage (0009-0009-1447-6214, University of Gondar), Tigabu Eskeziya Zerihun (Debre Tabor University), Abriham Zegeye (University of Gondar), Adem Tsegaw Zegeye, Tarekegn Cheklie Zeleke (0000-0002-6120-3552, University of Gondar), Abel Temeche Kassaw (0009-0008-3431-8187, Debre Tabor University) |
| Ano | 2025 |
| Volume | 25 |
| Fascículo | 1 |
| Páginas | 901-901 |
| Data de publicação | 2025-03-06 |
| Peer Reviewed | Sim |
| Open Access | Sim |
| Tipo | ARTICLE |
| Periódico | BMC Public Health (JOURNAL) |
| Identificadores do periódico | ISSN: 1471-2458 • E-ISSN: 1471-2458 |
| Editora | BioMed Central (PUBLISHER • GB) |
| DOI | 10.1186/s12889-025-22007-9 |
| PMID | 40050868 |
| OpenAlex | W4408190844 |
| Idioma | EN |
| Citações recebidas | 1 |
| Referências citadas | 34 |
The findings highlight the potential of machine learning to identify critical predictors of skilled birth attendance to inform targeted interventions. Addressing socioeconomic and educational disparities, enhancing healthcare access, and implementing tailored cessation programs are crucial to enhance skilled birth attendance in this vulnerable population
Attendance · Developing country · Economic growth · Environmental health · Interpretability · Machine learning · Population · Public health · Random forest · Reproductive health · Computer Science · Demography · Global Health Care Issues · Global Maternal and Child Health · Healthcare Systems and Reforms · Medicine · Artificial Intelligence
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| Obras citantes distintas | 1 |
|---|---|
| Citações por ano | 1 |
| Intervalo de citações | 2025 - 2025 (1) |
| Velocidade de citação | recent |
| Altamente citado | Não |
| Tipos de citação | Neutras: 1 |