Missing data is a common issue in medical research, and is often caused by human factors during data collection in epidemiology and clinical studies, which mainly involve individuals as subjects.

Selecting appropriate machine learning (ML) methods for domain-specific tasks remains a persistent challenge, particularly in medicine where datasets are often small, heterogeneous, and incomplete.