Optimizing machine learning models for predicting anemia among under-five children in Ethiopia: insights from Ethiopian demographic and health survey data.
In general, the random forest algorithm emerged as the preferred model for predicting anemia in children under five. The model exhibited a specificity of 79.26%, sensitivity of 83.07%, positive predictive value of 80.02%, negative predictive value of 82.40%, and an area under the curve of 81.80%.
