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

TL;DR

This paper evaluates machine learning-based imputation methods to handle missing data in breast cancer datasets, aiming to improve prediction accuracy of treatment outcomes and chemotherapy-related amenorrhoea.

Contribution

It introduces an efficient machine learning approach for imputing missing values in breast cancer data, enhancing the quality of data analysis and prediction performance.

Findings

Machine learning imputation improves data quality.

Enhanced prediction accuracy for breast cancer treatment outcomes.

Better handling of missing data compared to traditional methods.

Abstract

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.