Predicting Levels of Anemia among Adolescents in Ethiopia Using homogeneous ensemble Machine Learning algorithm

Anemia significantly impacts adolescent girls health and quality of life in Ethiopia. Effective interventions require identifying key risk factors and predicting anemia severity. While traditional studies primarily use statistical methods, this research aims to leverage machine learning models to predict anemia risk and analyze contributing socio-economic, environmental, and cultural factors. We applied machine learning models, including Random Forest, Extra Trees, CatBoost, XGBoost, and AdaBoost, to predict anemia severity using features from the Ethiopian Demographic and Health Survey (EDHS). Performance was evaluated using accuracy, ROC AUC, precision, recall, and F1-score, with feature importance analysis to identify key anemia risk factors. Random Forest and Extra Trees outperformed others, achieving accuracy rates of 82.51% and 82.41% and ROC AUC scores of 94.87% and 94.48%, respectively. CatBoost showed competitive performance (80.99% accuracy, 93.08% ROC AUC). XGBoost and AdaBoost were less effective. Key risk factors included region, education, wealth index, household size, and altitude. Random Forest and Extra Trees are effective for predicting anemia severity and identifying key socio-economic and environmental risk factors. Interventions focusing on education, healthcare access, and nutrition are vital to reducing anemia prevalence among adolescent girls in Ethiopia. Future work should refine models and expand datasets for improved public health outcomes.