A Hybrid Approach for Missing Data Imputation using Polynomial Interpolation and Backfill

Objectives: To propose a method for imputing missing values within non-linear datasets with class imbalance issues. Methods: The proposed methodology includes two stages of imputation through the Polynomial with Backfill (PoBa) process. This imputation technique applies quadratic polynomial interpolation for individual class type missing value estimation as well as implementing a backfill strategy to complete remaining gaps. The evaluation process utilized six ML models to assess dataset imputations under different non-linear and class imbalance conditions within two publicly available datasets. Findings: The PoBa method showed superior performance than HPCNN baseline in all the metric evaluations for different degrees of missingness. The Wine Quality dataset received an accuracy score of 0.807 and MAE of 0.193 and RMSE of 0.439 when feeding PoBa to Multi-Layer Perceptron (MLP) under 30% missing data conditions. The PoBa method reached 0.950 accuracy along with 0.050 MAE and 0.224 RMSE in the heart disease dataset despite its imbalanced characteristics. The experiments demonstrate that PoBa effectively preserves data organization and improves predictive performance while handling large real-world datasets. Novelty: The method proposes a class-specific combination of polynomial interpolation with backfill to handle missing data in an efficient and interpretable way. Keywords: Missing Data, Polynomial Interpolation, Backfill Imputation, Machine Learning, Class Imbalance