Eliyas Addisu Taye
Biographic Data
| ID | 7759137 |
|---|---|
| NAME | Eliyas Addisu Taye |
| GIVEN NAMES | Eliyas Addisu |
| FAMILY NAME | Taye |
| SIGNATURE | TAYE E A |
| AFFILIATIONS | University of Gondar |
| ORCID | 0009-0005-1561-072X |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 6 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Explainable machine learning for predicting childhood anemia in Sub-Saharan Africa using population-based DHS Data (2016–2024)
Childhood anemia remains a major public health challenge in Sub-Saharan Africa, adversely affecting physical growth, cognitive development, and child survival. The pooled prevalence across 26 countries exceeds 60%, underscoring the need for accurate and scalable prediction tools to support targeted interventions.This study used pooled Demographic and Health Survey (DHS) data (2016–2024) from 26 Sub-Saharan African countries, including 110,251 chi…
Geospatial inequalities and determinants of caesarean section delivery in sub-Saharan Africa
Background Cesarean section is a lifesaving obstetric intervention when medically indicated; however, its utilization remains unequal across sub-Saharan Africa (SSA). Although the World Health Organization recommends cesarean section rates of 10–15%, access remains insufficient in many low-resource settings and excessive in others. Understanding geographic patterns and drivers is essential for maternal health planning.Objective(s) To examine the …
Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia
Introduction: Khat chewing is a significant public health issue in Ethiopia, influenced by various demographic factors. Understanding the prevalence and determinants of khat chewing practices is essential to developing targeted interventions. Therefore, this study aimed to predict khat chewing practices and their determinant factors among men aged 15 to 59 years in Ethiopia using a machine learning algorithm. Methods: This study used data from th…
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
Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries
Nearly six out of ten women received informed contraceptive choice, the magnitude is highest in Burkina Faso and lowest in Mali. Moreover, the highest spatial clustering of informed choice of contraceptive was observed in Burkina Faso while the lowest clustering was found in Angola. The LGBM classifier outperformed among machine learning algorithms and achieved 73% accuracy and an AUC of 0.80. Key factors influencing informed contraceptive choice…
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…
No prominent works on this page.
Machine learning algorithms to predict khat chewing practice and its predictors among men aged 15 to 59 in Ethiopia
Introduction: Khat chewing is a significant public health issue in Ethiopia, influenced by various demographic factors. Understanding the prevalence and determinants of khat chewing practices is essential to developing targeted interventions. Therefore, this study aimed to predict khat chewing practices and their determinant factors among men aged 15 to 59 years in Ethiopia using a machine learning algorithm. Methods: This study used data from th…
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
Application of machine learning algorithms to model predictors of informed contraceptive choice among reproductive age women in six high fertility rate sub Sahara Africa countries
Nearly six out of ten women received informed contraceptive choice, the magnitude is highest in Burkina Faso and lowest in Mali. Moreover, the highest spatial clustering of informed choice of contraceptive was observed in Burkina Faso while the lowest clustering was found in Angola. The LGBM classifier outperformed among machine learning algorithms and achieved 73% accuracy and an AUC of 0.80. Key factors influencing informed contraceptive choice…
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…
Explainable machine learning for predicting childhood anemia in Sub-Saharan Africa using population-based DHS Data (2016–2024)
Childhood anemia remains a major public health challenge in Sub-Saharan Africa, adversely affecting physical growth, cognitive development, and child survival. The pooled prevalence across 26 countries exceeds 60%, underscoring the need for accurate and scalable prediction tools to support targeted interventions.This study used pooled Demographic and Health Survey (DHS) data (2016–2024) from 26 Sub-Saharan African countries, including 110,251 chi…
Geospatial inequalities and determinants of caesarean section delivery in sub-Saharan Africa
Background Cesarean section is a lifesaving obstetric intervention when medically indicated; however, its utilization remains unequal across sub-Saharan Africa (SSA). Although the World Health Organization recommends cesarean section rates of 10–15%, access remains insufficient in many low-resource settings and excessive in others. Understanding geographic patterns and drivers is essential for maternal health planning.Objective(s) To examine the …
Medicine (4 works) · Public health (4 works) · Random forest (4 works) · Demography (3 works) · Environmental health (3 works) · Global Maternal and Child Health (3 works) · Machine learning (3 works) · Biostatistics (2 works) · Computer Science (2 works) · Demography (2 works)