Tag
This paper develops a multi-dimensional framework to evaluate discrimination, calibration, interpretability, and algorithmic fairness for machine learning-based type 2 diabetes risk prediction models, revealing significant performance degradation under real-world distribution shifts and biases by age and obesity.