Predicting childhood anaemia in Ghana with explainable machine learning: A national survey analysis.
Machine learning applied to health data can accurately predict childhood anemia in Ghana and explain its predictions, helping guide public health screening efforts.
This study applied explainable machine learning to Ghana's national survey data to predict childhood anaemia. The approach demonstrates how ML can support public health screening in resource-limited settings with interpretable results.
What the study was
- Study design
- Cross-sectional ML analysis of national survey data
- Population
- Children in Ghana (national survey)
- Category
- Diagnostics
- Maturity
- Exploratory
- Journal
- Digital Health
Why it surfaced
ML-based hematology diagnostics in underserved population. Lower impact venue but relevant to CBC/ML watchlist.
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