  Clinical decision support using data mining techniques offers more
intelligent way to reduce the decision error in the last few years. However,
clinical datasets often suffer from high missingness, which adversely impacts
the quality of modelling if handled improperly. Imputing missing values
provides an opportunity to resolve the issue. Conventional imputation methods
adopt simple statistical analysis, such as mean imputation or discarding
missing cases, which have many limitations and thus degrade the performance of
learning. This study examines a series of machine learning based imputation
methods and suggests an efficient approach to in preparing a good quality
breast cancer (BC) dataset, to find the relationship between BC treatment and
chemotherapy-related amenorrhoea, where the performance is evaluated with the
accuracy of the prediction.
