Tirualem Zeleke Yehuala
Biographic Data
| ID | 7798903 |
|---|---|
| NAME | Tirualem Zeleke Yehuala |
| GIVEN NAMES | Tirualem Zeleke |
| FAMILY NAME | Yehuala |
| SIGNATURE | YEHUALA T Z |
| AFFILIATIONS | University of Gondar |
| ORCID | 0009-0007-3909-8825 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 8 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| 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…
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
Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa
Background: Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed to predict the absence of maternal healthcare-seeking behavior and identify its associated factors among children unde…
Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram
Introduction: Women are more vulnerable to HIV infection due to biological and socioeconomic reasons. Developing a predictive model for these vulnerable populations to estimate individualized risk for HIV infection is relevant for targeted preventive interventions. The objective of the study was to develop and validate a risk prediction model that allows easy estimations of HIV infection risk among sexually active women in Ethiopia. Methods: Data…
Factors associated with the co-utilization of oral rehydration solution and zinc for treating diarrhea among under-five children in 35 sub-saharan Africa countries
Only less than half of under-five children with diarrhea in SSA were treated with a combination of ORS and zinc. Thus, strengthening information dissemination through mass media, and community-level health education programs are important to scale up the utilization of the recommended combination treatment. Furthermore, increasing health insurance coverage, and establishing strategies to address the community with difficulty in accessing health f…
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Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa
Background: Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and frequency of healthcare services for their children. Therefore, this study aimed to predict the absence of maternal healthcare-seeking behavior and identify its associated factors among children unde…
Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram
Introduction: Women are more vulnerable to HIV infection due to biological and socioeconomic reasons. Developing a predictive model for these vulnerable populations to estimate individualized risk for HIV infection is relevant for targeted preventive interventions. The objective of the study was to develop and validate a risk prediction model that allows easy estimations of HIV infection risk among sexually active women in Ethiopia. Methods: Data…
Factors associated with the co-utilization of oral rehydration solution and zinc for treating diarrhea among under-five children in 35 sub-saharan Africa countries
Only less than half of under-five children with diarrhea in SSA were treated with a combination of ORS and zinc. Thus, strengthening information dissemination through mass media, and community-level health education programs are important to scale up the utilization of the recommended combination treatment. Furthermore, increasing health insurance coverage, and establishing strategies to address the community with difficulty in accessing health f…
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…
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
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…
Child Nutrition and Water Access (5 works) · Global Maternal and Child Health (5 works) · Logistic regression (5 works) · Medicine (5 works) · Computer Science (4 works) · Public health (4 works) · Random forest (4 works) · Biostatistics (3 works) · Demography (3 works) · Environmental health (3 works)