Nebebe Demis Baykemagn
Biographic Data
| ID | 7781009 |
|---|---|
| NAME | Nebebe Demis Baykemagn |
| GIVEN NAMES | Nebebe Demis |
| FAMILY NAME | Baykemagn |
| SIGNATURE | BAYKEMAGN N D |
| AFFILIATIONS | University of Gondar |
| ORCID | 0009-0008-9403-6915 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 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…
Use of explainable machine learning in risk classification of Cesarean section delivery
Cesarean section (CS) delivery is an important surgical intervention for reducing maternal and neonatal morbidity and mortality when medically indicated; however, substantial inequalities in its utilization persist across Sub-Saharan Africa (SSA). This study evaluated the performance of explainable machine learning (ML) algorithms in classifying cesarean section delivery patterns using Demographic and Health Survey (DHS) data from ten SSA countri…
Machine learning algorithms to predict feeding practices during diarrheal disease and its determinants among under-five children in East Africa
Background: Diarrhea is the leading cause of childhood malnutrition. Although replacement, continued feeding, and increasing appropriate fluid at home during diarrhea episodes are the cornerstones of treatment packages, food and fluid restrictions are common during diarrheal illnesses in Africa. To fill the methodological and current evidence gaps, this study aimed to build models and predict determinants to increase feeding practices of children…
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…
Predicting malnutrition in PLWHIV using machine learning in gondar, Ethiopia
ML models offer a robust approach for predicting malnutrition risk in PLWHIV patients. The integration of these tools into routine care could enhance nutritional management, particularly in low-resource settings. Further studies are needed to confirm these findings and improve the deployment of the model in clinical settings
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
Intention to use mobile phone-based TB screening among HIV patients in Debre Tabor Town public health facilities, Northwest Ethiopia
In conclusion, approximately 70.7% of HIV clients intend to use mobile phone-based TB screening services, which is higher compared to previous studies. Factors such as employment status, experience reading received text messages, perceived usefulness, perceived ease of use, and having clinical follow-ups at a hospital were found to be significantly associated with the intention to use mobile phone-based TB screening. Healthcare providers, mobile …
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Machine learning algorithms to predict feeding practices during diarrheal disease and its determinants among under-five children in East Africa
Background: Diarrhea is the leading cause of childhood malnutrition. Although replacement, continued feeding, and increasing appropriate fluid at home during diarrhea episodes are the cornerstones of treatment packages, food and fluid restrictions are common during diarrheal illnesses in Africa. To fill the methodological and current evidence gaps, this study aimed to build models and predict determinants to increase feeding practices of children…
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…
Predicting malnutrition in PLWHIV using machine learning in gondar, Ethiopia
ML models offer a robust approach for predicting malnutrition risk in PLWHIV patients. The integration of these tools into routine care could enhance nutritional management, particularly in low-resource settings. Further studies are needed to confirm these findings and improve the deployment of the model in clinical settings
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
Intention to use mobile phone-based TB screening among HIV patients in Debre Tabor Town public health facilities, Northwest Ethiopia
In conclusion, approximately 70.7% of HIV clients intend to use mobile phone-based TB screening services, which is higher compared to previous studies. Factors such as employment status, experience reading received text messages, perceived usefulness, perceived ease of use, and having clinical follow-ups at a hospital were found to be significantly associated with the intention to use mobile phone-based TB screening. Healthcare providers, mobile …
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…
Use of explainable machine learning in risk classification of Cesarean section delivery
Cesarean section (CS) delivery is an important surgical intervention for reducing maternal and neonatal morbidity and mortality when medically indicated; however, substantial inequalities in its utilization persist across Sub-Saharan Africa (SSA). This study evaluated the performance of explainable machine learning (ML) algorithms in classifying cesarean section delivery patterns using Demographic and Health Survey (DHS) data from ten SSA countri…
Logistic regression (4 works) · Medicine (4 works) · Public health (4 works) · Random forest (4 works) · Biostatistics (3 works) · Child Nutrition and Water Access (3 works) · Computer Science (3 works) · Environmental health (3 works) · Epidemiology (3 works) · Global Maternal and Child Health (3 works)