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

This study introduces yvsoucom-iterkit, a deterministic and log-driven automated machine learning framework for interpretable pipeline optimization in healthcare risk prediction. It enables reproducible analysis of pipeline components, revealing that performance is driven by a small subset of interacting elements like augmentation, model choice, and imbalance handling.