Open access
Jul 2026
A Data-Driven Approach for Handling Missing Data in a Real Multiple Sclerosis Dataset Based on Machine Learning
The proposed Sequential Multiple Imputation Bootstrapping (SMIB) framework provides a robust and clinically aligned strategy for handling real-world missing values in MS datasets, improving imputation accuracy while preserving feature dependencies and feature relationships.
Shima Pilehvari, Wei Peng, Mohammadali Sahraian et al.
· Sclerosis · 0 citations