Abstract Objective: Chemotherapy-induced myelosuppression (CIM) is the most common adverse reaction of tumor chemotherapy and may lead to treatment delay, dose reduction, life-threatening infection, and poor prognosis. Current predictive methods remain suboptimal, with insufficient interpretability and limited clinical application. This study aimed to develop and validate an interpretable machine learning model for early CIM risk prediction using routine laboratory data.
