Abriham Zegeye
Dados Biográficos
| ID | 7759140 |
|---|---|
| NOME | Abriham Zegeye |
| PRENOMES | Abriham |
| SOBRENOME | Zegeye |
| ASSINATURA | ZEGEYE A |
| AFILIAÇÕES | University of Gondar |
| VERIFICADO | Não |
| TOTAL DE OBRAS | 3 |
| TOTAL DE CITAÇÕES | 0 |
| TOTAL COMO AUTOR | 3 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2025 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2025 |
| ÍNDICE H | 0 |
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
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
Predicting home delivery and identifying its determinants among women aged 15–49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016–2023
The random forest machine learning model provides greater predictive power for estimating home delivery risk factors. To reduce the prevalence of home delivery, this finding recommends to emphasis on improving antenatal care services, education, and awareness about health facility delivery
Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries
This study utilized a Random Forest machine learning algorithm to identify key predictors of tobacco use among pregnant women across 26 Sub-Saharan African countries. Significant factors included maternal literacy, education, wealth index, and healthcare access, highlighting systemic inequities contributing to tobacco dependency during pregnancy. These findings advocate for policies addressing educational disparities, economic inequalities, and b…
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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
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
Predicting home delivery and identifying its determinants among women aged 15–49 years in sub-Saharan African countries using a Demographic and Health Surveys 2016–2023
The random forest machine learning model provides greater predictive power for estimating home delivery risk factors. To reduce the prevalence of home delivery, this finding recommends to emphasis on improving antenatal care services, education, and awareness about health facility delivery
Random forest algorithm for predicting tobacco use and identifying determinants among pregnant women in 26 sub-Saharan African countries
This study utilized a Random Forest machine learning algorithm to identify key predictors of tobacco use among pregnant women across 26 Sub-Saharan African countries. Significant factors included maternal literacy, education, wealth index, and healthcare access, highlighting systemic inequities contributing to tobacco dependency during pregnancy. These findings advocate for policies addressing educational disparities, economic inequalities, and b…
Environmental health (3 obras) · Medicine (3 obras) · Public health (3 obras) · Biostatistics (2 obras) · Computer Science (2 obras) · Epidemiology (2 obras) · Global Maternal and Child Health (2 obras) · Healthcare Systems and Reforms (2 obras) · Machine learning (2 obras) · Pathology (2 obras)