Value of an automated machine learning model with post-hoc explanation for predicting healthcare-seeking delays among residents in Tibetan regions

A high-performance model with post-hoc SHAP interpretation accurately identifies geographical, cultural, and healthcare resource variables to accurately identify high-risk populations. The developed clinical decision support system enables risk computation through modular interfaces, providing an evidence-based tool for optimizing hierarchical diagnosis and resource allocation in Tibetan healthcare.