Learning dynamics vs. aggregate metrics: clinical applicability of machine learning survival prediction in eye cancer

Introduction: The application of machine learning in healthcare requires models that demonstrate not only acceptable classification performance but also trustworthy learning behavior suitable for clinical deployment. Class imbalance represents a pervasive challenge in medical datasets, where patients with favorable outcomes substantially outnumber those with adverse events. Materials and methods: This study compared two ensemble learning approaches for five-year survival prediction in eye cancer: CatBoost, a gradient boosting algorithm employing balanced class weights, and RUSBoost, an algorithm integrating random undersampling directly within the boosting framework. Model evaluation extended beyond aggregate performance metrics to include systematic assessment of learning dynamics throughout training. Results: Both classifiers achieved comparable discriminative ability on held-out test data, with area under the receiver operating characteristic curve values of approximately 0.78. Confusion matrix analysis revealed that both models demonstrated acceptable classification rates with expected gradual decreases from training through validation to tes