A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications.

Background: Missing values in datasets present significant challenges for data analysis, particularly in the medical field where data accuracy is crucial for patient diagnosis and treatment. Although MissForest (MF) has demonstrated efficacy in imputation research and recursive feature elimination (RFE) has proven effective in feature selection, the potential for enhancing MF through RFE integration remains unexplored.

Methods: This study introduces a novel imputation method, "recursive feature elimination-MissForest" (RFE-MF), designed to enhance imputation quality by reducing the impact of irrelevant features.