Abstract

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: We propose an innovative data-driven approach called the Sequential Multiple Imputation Bootstrapping (SMIB) model, which orders imputation based on feature correlation and incorporates bootstrapping to enhance stability and generalizability. Relevant features were identified using RF importance and Mutual Information scores and imputed using a hybrid machine learning framework that combines Random Forest (RF), Multilayer Perceptron (MLP), k-Nearest Neighbors (kNN), and a multiple imputation (MI) algorithm. The proposed method was evaluated using 15-fold cross-validation and a masking-based evaluation strategy. Model performance was assessed using accuracy, precision, recall, specificity, F1 score, Mean Squared Error (MSE), Mean Absolute Error (MAE), and R2. Results: RF-based SMIB achieved superior performance, with final imputation accuracy reaching up to 97% for categorical outcomes and strong numerical performance (R2 up to 0.999; MSE as low as 2.48 × 10−5). Sequential ordering and weighted bootstrapping improved stability in imbalanced clinical data under real-world missingness. Compared with the widely adopted Multiple Imputation by Chained Equations (MICE) approach, the proposed SMIB framework consistently demonstrated superior predictive performance across all evaluated categorical and numerical outcomes. Conclusions: The 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. The method supports reliable predictive analytics in healthcare contexts.

IPC Classification

Keywords