Imputation techniques on missing values in breast cancer treatment and fertility data
This work addresses data quality issues in clinical decision support for breast cancer treatment, but it is incremental as it applies existing imputation techniques to a specific domain.
The study tackled the problem of high missingness in clinical datasets by evaluating machine learning-based imputation methods to improve data quality for predicting the relationship between breast cancer treatment and chemotherapy-related amenorrhoea, achieving results evaluated with prediction accuracy.
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.