A Data-Driven Approach for Handling Missing Data in a Real Multiple Sclerosis Dataset Based on Machine Learning
Background and Objective: Reliable medical research depends on data integrity, yet clinical datasets often contain real and systematically missing values. This study aimed to develop a robust, clinically realistic imputation framework for a raw Multiple Sclerosis (MS) dataset affected by real-world missingness. Methods...