Evaluates data efficiency in lung cancer risk prediction using a super-stacking ensemble. Trains models on progressively reduced fractions of the PLCO dataset while keeping a fixed test set, analyzing performance stability, degradation, and robustness under limited data.

Early lung cancer risk prediction using machine-learning models trained on the PLCO dataset. End-to-end pipeline including data cleaning, feature preprocessing, multiple ML baselines, super-stacking ensembles, class-imbalance handling, optimal threshold tuning for high recall, comprehensive evaluation, and SHAP-based model interpretability.