Missing data imputation using statistical and machine learning methods in a real breast cancer problem

摘要

ObjectivesMissing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. This work evaluates the performance of several statistical and machine learning imputation methods that were used to predict recurrence in patients in an extensive real breast cancer data set.