Few-shot Cross-country Generalization of Tabular Machine Learning and Foundation Models for Childhood Anemia Prediction under Distribution Shift
arXiv:2605.26589v1 Announce Type: new Abstract: Childhood anemia affects around 40% of children aged 6-59 months globally and arises from heterogeneous factors, limiting model generalizability. We evaluate a transformer-based tabular foundation model against classical supervised methods under cross-country and data-scarce settings. We used DHS data from 16 countries across Africa, Asia, Latin America, the Caucasus, and the Middle East (n=68,856). We compared Logistic Regression, XGBoost, LightGBM, and TabPFN v2.6. Performance was assessed using AUC-ROC, Brier score, and ECE. Generalization was
The increasing availability of large, diverse datasets and advancements in transformer architecture enable the development of foundation models for complex, real-world health challenges like childhood anemia, especially where data is scarce or distributed across many regions.
This research demonstrates the potential for advanced AI, particularly foundation models, to address critical global health issues in resource-constrained environments, improving diagnostic accuracy and intervention strategies across diverse populations.
The application of sophisticated AI models is expanding from general-purpose tasks to highly specific, impactful domains like public health, showing promise in overcoming data scarcity and distribution shifts that have historically limited AI's utility in these areas.
- · Global Health Organizations
- · Developing Nations
- · AI/ML Research Communities
- · Public Health Informatics
- · Traditional Manual Diagnostic Processes
- · Single-country Clinical Trials
- · Underserved Populations with Limited Data Infrastructure
Improved targeted interventions for childhood anemia globally due to more accurate prediction models.
Accelerated adoption of AI-driven diagnostics and public health tools in low- and middle-income countries.
Reduced health disparities and improved child mortality rates globally through AI-powered predictive analytics aiding policy and resource allocation.
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