Prediction of radionuclide diffusion enabled by missing data imputation and ensemble machine learning

Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of machine learning models. A regression-based missing data imputation method using light gradient boosting machine algorithm was employed to impute over 60% of the missing data.