risk-prediction

This study evaluates five machine learning classifiers for chronic kidney disease risk prediction, finding that near-perfect internal performance fails under distribution shift. It emphasizes the need for calibration stability and conformal coverage transfer before clinical deployment.

This paper introduces yvsoucom-iterkit, a deterministic, log-driven AutoML framework for reproducible pipeline optimization in healthcare risk prediction, evaluated on diabetes and stroke datasets with over 18,000 pipeline configurations, achieving strong performance and revealing structured search spaces with component redundancy.