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In developing countries, one-quarter of young women have suffered from anemia. However, the available studies in Ethiopia have been usually used the traditional stastical methods. Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of anemia among youth girls in Ethiopia. A total of 5642 weighted samples of young girls from the 2016 Ethiopian Demographic and Health Survey dataset were utilized. The data underwent preprocessing, with 80% of the observations used for training the model and 20% for testing. Eight machine learning algorithms were employed to build and compare models. The model performance was assessed using evaluation metrics in Python software. Various data balancing techniques were applied, and the Boruta algorithm was used to select the most relevant features. Besides, association rule mining was conducted using the Apriori algorithm in R software. The random forest classifier with an AUC value of 82% outperformed in predicting anemia among all the tested classifiers. Region, poor wealth index, no formal education, unimproved toilet facility, rural residence, not used contraceptive method, religion, age, no media exposure, occupation, and having more than 5 family size were the top attributes to predict anemia. Association rule mining was identified the top seven best rules that most frequently associated with anemia. The random forest classifier is the best for predicting anemia. Therefore, making it potentially valuable as decision-support tools for the relevant stakeholders and giving emphasis for the identified predictors could be an important intervention to halt anemia among youth girls. [ABSTRACT FROM AUTHOR]

Attrition rate is higher in developing nations and it leftovers a major obstacle to enhance the benefits of therapy and achieve the 90-90-90 plan targets. Despite this fact, data on the incidence and its predictors of attrition among human immune deficiency virus infected children on antiretroviral therapy are limited in developing countries including Ethiopia especially after the test and treat strategy implemented. This study aimed to assess the incidence and predictors of attrition among human immune deficiency virus infected children on antiretroviral therapy in Amhara Comprehensive Specialized Hospitals, Northwest Ethiopia. A retrospective follow-up study was conducted among 359 children on ART from June 14, 2014, to June 14, 2022. Study participants were selected using simple random sampling method and the data were collected using Kobo Toolbox software and analysis was done by STATA version 14. Both bi-variable and multivariable Cox regression models were fitted to ascertain predictors. Lastly, an AHR with a 95% CI was computed and variables with a p-value of < 0.05 were took an account statistically key predictors of attrition. The overall incidence of attrition rate was 9.8 (95% CI 7.9, 11.9) per 100 PYO. Children having baseline hemoglobin < 10 mg/dl (AHR 3.94; 95% CI 2.32, 6.7), suboptimal adherence (AHR 1.96; 95% CI 1.23, 3.13), baseline opportunistic infection (AHR 1.8; 95% CI 1.17, 2.96), and children who had experienced drug side effects (AHR 8.3; 95% CI 4.93, 13.84) were established to be a significant predictors of attrition. The attrition rate was relatively high. Decreased hemoglobin, suboptimal adherence, presence of drug side effects and baseline opportunistic infection were predictors of attrition. Therefore, it is crucial to detect and give special emphasis to those identified predictors promptly., (© 2024. The Author(s).)