A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction

In three linesyvsoucom-iterkit, a deterministic log-driven AutoML framework, optimizes medical risk prediction pipelines across 18,000+ configurations. On Pima and Stroke datasets, augmentation (0.454), model choice (0.198), and imbalance handling (0.101–0.406) are key drivers. Ensembles achieve F1 0.89–0.94 with cross-seed robustness (variability 0.023–0.026).

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