Stunting is an indicator of the devastating result of malnutrition in early childhood. The effects of childhood stunting are irreparable physical and cognitive harm. It is an issue of the great public health importance throughout Sub-Saharan African countries including Ethiopia. Therefore, identification of the risk factors of child stunting from recent data is very important for timely intervention. The 2016 Ethiopian Demographic and Health Survey data were used for this study. A generalized linear mixed model which is an extension of the general linear model was employed to identify socioeconomic, demographic, environmental and health related risk factors for stunted under-five children. The result shows that the age and sex of the child, preceding birth interval, mother’s body mass index, household wealth index, mother’s education level, breastfeeding period, type of toilet facility, use of internet and source of drinking water were the major determinants of stunting of under-five children in Ethiopia. The study indicated that children from undernourished mothers, who are not breastfeeding, from poor households, households that have no toilet facilities, who are male, older age (between 12 to 59 months), who have illiterate mother and short birth spacing were associated with stunting problems. Therefore, family planning education and policy is required for the country to improve on under-five age stunting problems.

Childhood morbidities are a major cause of mortality of children in the developing countries particularly in Ethiopia. Regardless of the noticeable improvement in the reduction of under-five death in Ethiopia, childhood diarrhea and fever are still the leading cause of death. In Ethiopia, the burden of child mortality is alarming and calls for determined efforts in combating such health problems. Therefore, this study aimed to investigate the risk factors for childhood morbidity specifically for diarrhea and fever. To gain insight into children’s health issues, the 2016 Ethiopian Demographic and Health Survey data were used. Among the marginal models, alternating logistic regression that is an extension of the generalized estimating equation model was used to investigate the risk factors of childhood morbidity explicitly for diarrhea and fever. The results show that the child’s sex, child’s age, anemia level, husband education level, mother’s work status, mother’s marital status, breastfeeding status and region are all chosen significant risk factors related with childhood diarrhea disease and fever disease. The study indicated that male children, 0–11 months aged children, 12–23 months aged children, anemic children, husband with a lower education, mothers paid employment, non-breastfed children, regions of Amhara, Afar, Dire Dawa, Benishangul, Gambela, Oromia, SNNPR, Somali and Tigray were significantly associated with higher odds of morbidity in Ethiopia. Therefore, there is a need for children morbidity interventions intended to improve child health outcomes in the country.

Breast cancer is the most frequently diagnosed cancer among women and persists as a societal problem worldwide. It remains a leading cause of cancer associated morbidity and mortality, specifically in low- and middle-income countries where access to timely diagnosis and treatment is often limited. This study aims to compare survival and classical machine learning models for predicting breast cancer survival in Ethiopia to identify approaches that balance predictive accuracy with interpretability. The study utilized retrospective data from 1164 women treated at Tikur Anbesa Specialized Hospital and Hiwot Fana Specialized University Hospital between 2019 and 2024. Methods like Kaplan–Meier estimation, Cox proportional hazards, random survival forests (RSF), DeepSurv, and classical machine learning (SVM, XGBoost, LGBM, and RF) classifiers were used with evaluation metrics such as AUC, C-index, and Integrated Brier Score (IBS). The Shapley additive explanation approach was used to ensure the interpretability of results from models such as RSF, DeepSurv, and random forests (RF). It allowed the identification of important predictors of breast cancer outcome by indicating consistent predictors across models. The findings demonstrated that random survival forest and random forest achieved the highest performance (C-index: 0.754; IBS: 0.091) and (0.729 ± 0.006), respectively, outperforming the other models under consideration. The Shapley Additive Explanations (SHAP) analysis for the RSF model showed that age, tumour size, metastasis, stage, comorbidities, and marital status as the most important predictors of breast cancer survival. Furthermore, the SHAP analysis for the RF model indicated that the higher age category (45 and above), metastasis status (M1), stage four, and larger tumour size contribute a strong influence on predictions. Among the machine learning models, the random forest algorithm effectively identifies the key predictors of breast cancer outcomes. For the survival analysis methods, the RSF offers robust capabilities for handling time-to-event data and censoring, making it well-suited for accurate survival prediction. By combining these approaches, we were able to gain clearer insights and better identify the key factors influencing breast cancer prognosis. This study highlights the value of data-driven methods in helping healthcare professionals identify high-risk patients with greater precision and take timely, informed actions to support their care.

Malnutrition and morbidity are substantial problems in Ethiopia and are still pervasive and persistent. Despite this, there has been scant research on the coexistence of malnutrition and morbidity indicators. Moreover, previous studies were based on all data records of measurements from manifest data. Thus, this study aims to identify the correlates and coexistence of child malnutrition and morbidity within this country. Cross-sectional data which is collected by Ethiopia Demographic and Health Survey were used. The generalized structural equation models were used to examine the association between child malnutrition, morbidity, and potential risk factors. The generalized structural equation models help to provide latent effects of child malnutrition and morbidity within a combined modeling framework. In addition, the generalized structural equation models make it possible to analyze malnutrition as a mediator of the association between selected risk factors and latent variable morbidity. The data analysis was done using SPSS AMOS and R software. The analysis indicated that children born to nourished mothers (AOR = 0.71, 95% CI 0.68–0.75), born to enough birth space between 24 and 47 months and (AOR = 0.93, 95% CI 0.88–0.99), 48 months and above (AOR = 0.71, 95% CI 0.65–0.76), being from middle-income households (AOR = 0.85, 95% CI 0.78–0.91), high-income households (AOR = 0.66, 95% CI 0.61–0.72), from mother with primary or secondary (AOR = 0.79, 95% CI 0.75–0.85) and higher education level (AOR = 0.57, 95% CI 0.41–0.78) were less affected by malnutrition. It also revealed that a child born second to third (AOR = 0.87, 95% CI 0.77–0.99), fourth and higher (AOR = 0.88, 95% CI 0.79–0.99) and children from a husband-educated higher level (AOR = 0.76, 95% CI 0.64–0.89) were less likely to be ill. Children who breastfeed (AOR = 0.98, 95% CI 0.80–0.99), from nourished mothers (AOR = 0.96, 95% CI 0.94–0.097), from middle income (AOR = 0.97, 95% CI 0.96–0.99), high-income households (AOR = 0.94, 95% CI 0.93–0.96), birth spacing 24–47 months (AOR = 0.99, 95% CI 0.98–1.00) and 48 months and above (AOR = 0.96, 95% CI 0.94–0.97) were indirectly affected by morbidity via malnutrition. This investigation has revealed that childhood malnutrition and morbidity remain major child health challenges in Ethiopia with demographic, socioeconomic, maternal, child, and geographic variables playing significant roles. Efforts to resolve these issues need to take these factors into account. Therefore, malnutrition and morbidity prevention should include encouraging birth spacing, mother education programs, and breastfeeding practices.

Background: Addressing the issues of childhood comorbidity remains a crucial global public health issue due to its consequences in child wellbeing. This study aims to account for nonlinear, spatial effect and to evaluate spatial variation in childhood co-morbidity at cluster level while controlling for important risk factors. Methods: Using the 2016 Ethiopia DHS data, a multinomial logistic model was assessed by linear, nonlinear and random effects. The study also employed a spatial analysis tool which is Getis-Ord to identify hotspot areas of child comorbidity at the cluster level. The model with fixed, nonlinear and spatial effects identified as the best model to identify risk factors related to the coexistence of childhood illnesses. Results: The results indicated that statistically significant high hotspots of comorbidity were found in Tigray and Oromia whereas low hotspots were found in Harari and Somali regions. Children between 10 and 15 months old were at high risk of co-morbidity in Ethiopia. Besides, our findings revealed that being male children, not-breastfed children, from households lack of toilet facility, children from households who use spring water, children born first, children from working mother, anemic children and children from uneducated mother are at high risk of multiple illnesses. Conclusions: Comorbidity in childhood is not random in the country, with high hotspots of comorbidity in the regions of Tigray and Oromia. The results show a critical upshot for a combined morbidity control method for decreasing children’s illnesses and death. The maps remain novel to design appropriate healthcare interventions at regional as well as cluster level. Regions with high hotspots of child comorbidity should be considered for health healthcare interventions.

The most prevalent and lethal kind of cancer in Ethiopia is breast cancer. The number of deaths from breast cancer is rising dramatically every year. It is the most common kind of cancer overall and the leading cause of death for women in Ethiopia. Considering this, this study aims to identify optimal machine learning algorithms that can predict the stages of breast cancer. Unlike traditional methods, machine learning approaches have proven to be powerful methods in early detection and prediction of breast cancer. In this study, we have used the breast cancer dataset that was collected from Hiwot Fana Specialized University Hospital and Tikur Anbesa Specialized Hospital from September 2019 to April 2024. We have applied machine learning algorithms on the preprocessed breast cancer dataset; which are random forest, logistic regression, decision tree, and hybrid machine learning algorithms (RF, DT, GBC and SVM). Based on the results obtained from each algorithm, we compared and evaluated the performance of each classifier using evaluation metrics like precision, recall, F1 score, and accuracy to identify the optimal machine-learning algorithm. In order to find the optimal algorithms and improve the accuracy of the model, 13 features were selected as inputs. The model performance evaluation was done using the train split test and the 10-fold cross-validation. The experimental results were based on dataset division (80:20) to predict stages of breast cancer. Python programing language and required libraries were used to analyze dataset. According to the comparative analysis obtained from the dataset, the random forest model performed well in both trains split test and the 10-fold cross validation performance evaluation and surpassed other experimented algorithms. It has better effects, and its recall, precision, accuracy, and F1-scores are equal, which is 99% using train split test and 97% using 10-fold cross validation. Thus, random forest is the optimal machine-learning algorithm that used to determine stages of breast cancer patients in Ethiopia.