Fetlework Gubena Arage
Biographic Data
| ID | 7759141 |
|---|---|
| NAME | Fetlework Gubena Arage |
| GIVEN NAMES | Fetlework Gubena |
| FAMILY NAME | Arage |
| SIGNATURE | ARAGE F G |
| AFFILIATIONS | University of Gondar |
| ORCID | 0009-0009-1447-6214 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Maternal education-related inequality in female genital mutilation/cutting among daughters aged 0–14 years in three East African countries
BACKGROUND: Globally, more than 230 million girls and women have undergone female genital mutilation/cutting (FGM/C). It is estimated that about one-fourth of them reside in East Africa. Studies highlight educational attainment as one of the major factors contributing to the practice. OBJECTIVE: This study aimed to assess maternal education-related inequality in FGM/C among daughters aged 0-14 years by using Demographic and Health Survey (DHS) da…
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
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…
Early postnatal care uptake and its associated factors following childbirth in East Africa—a Bayesian hierarchical modeling approach
Background: The postnatal period is a critical period for both mothers and their newborns for their health. Lack of early postnatal care (PNC) services during a 2-day period is a life-threatening situation for both the mother and the babies. However, no data have been examined for PNCs in East Africa. Hence, using the more flexible Bayesian multilevel modeling approach, this study aims to investigate the pooled prevalence and potential factors fo…
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Early postnatal care uptake and its associated factors following childbirth in East Africa—a Bayesian hierarchical modeling approach
Background: The postnatal period is a critical period for both mothers and their newborns for their health. Lack of early postnatal care (PNC) services during a 2-day period is a life-threatening situation for both the mother and the babies. However, no data have been examined for PNCs in East Africa. Hence, using the more flexible Bayesian multilevel modeling approach, this study aims to investigate the pooled prevalence and potential factors fo…
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
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…
Maternal education-related inequality in female genital mutilation/cutting among daughters aged 0–14 years in three East African countries
BACKGROUND: Globally, more than 230 million girls and women have undergone female genital mutilation/cutting (FGM/C). It is estimated that about one-fourth of them reside in East Africa. Studies highlight educational attainment as one of the major factors contributing to the practice. OBJECTIVE: This study aimed to assess maternal education-related inequality in FGM/C among daughters aged 0-14 years by using Demographic and Health Survey (DHS) da…
Medicine (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Developing country (2 works) · Environmental health (2 works) · Global Maternal and Child Health (2 works) · Machine learning (2 works) · Public health (2 works) · Random forest (2 works) · Reproductive health (2 works)