Benchmarking clinical risk prediction algorithms with ensemble machine learning for the non-invasive diagnosis of liver fibrosis in NAFLD

Ensemble machine-learning methods, like the superlearner, combine multiple models into a single one to enhance predictive accuracy. Here we explore the potential of the superlearner as a benchmarking tool for clinical risk prediction, illustrating the approach to identifying significant liver fibrosis among patients with NAFLD.