Anemia poses significant risks to both maternal and fetal health, contributing to over 115,000 maternal deaths and 591,000 prenatal deaths annually. A recent study in Ethiopia used machine learning to predict anemia levels in pregnant women, based on data from the Ethiopian Demographic Health Survey. Among the tested algorithms, the CatBoost model, combined with class decomposition, achieved a remarkable 97.6% accuracy.
Key risk factors like pregnancy duration, age, water source, and household size were crucial for predictions. This study shows the potential for AI in improving maternal health outcomes and offers a promising avenue for future research to target specific types of anemia.
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