Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models are difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. The researchers of this study assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection.

Find more at: https://scholar.google.com/citations?user=AwaT7tcAAAAJ&hl=en&oi=ao