Author

Shima Pilehvari

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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