Infant mortality across EU health systems: heterogeneity encoding, fixed effects regression, and machine learning in a small macro panel.

Introduction: The infant mortality rate is widely used in comparative health research, but less is known about how panel regression and machine-learning models perform in small macro-level health datasets when they are evaluated using comparable information and the same out-of-period test period. Methods: Using a balanced panel of 25 European Union countries observed annually from 2000 to 2022 (575 country-year observations, no missing data) assembled from OECD and World Bank sources, we separated an explanatory analysis from a predictive analysis. The explanatory layer used a two-way fixed effects regression with country-clustered standard errors. The predictive layer trained pooled linear regression, tuned Random Forest, Gradient Boosting, and a Multilayer Perceptron, with and without explicit country information, on 2000-2017 and evaluated them on a held-out 2018-2022 window, alongside two naive benchmarks (persistence and historical country mean) and two prediction-compatible fixed effects specifications. Hyperparameters were tuned by expanding-window temporal cross-validation within the training period, leaving the test window untouched. Results: Under common out-of-period evaluation, a simple persistence benchmark achieved the strongest predictive performance (R Conclusion: In this EU infant-mortality panel, much of the predictive signal came from persistent country-specific outcome history. Country information improved the tree-based models, while model flexibility also remained important. The strong within-panel fit of the fixed-effects model did not translate into strong out-of-period prediction.