Open access
Jul 2026
Explainable XGBoost model and nomogram for risk factor identification and risk prediction in cerebral small vessel disease: a machine learning-based retrospective cohort study
An interpretable XGBoost-based ML model that facilitates early risk stratification and targeted interventions for CSVD is validated, readily transferable to resource-limited settings and on embedding the nomogram into electronic-health-record decision support.
Xi Zhu, Xu-Hui Liu, Xujie Wang et al.
· Frontiers in Neurology · 0 citations