# Imputation techniques on missing values in breast cancer treatment and fertility data

arXiv: https://arxiv.org/abs/2011.09912 · HTML: https://mlsanity.com/paper/2011.09912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [On the Performance of Imputation Techniques for Missing Values on Healthcare Datasets](https://mlsanity.com/paper/2403.14687/) (2024)

## Abstract (arXiv)

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.