ArticleHealth information science and systems2019
Imputation techniques on missing values in breast cancer treatment and fertility data.
Article in Health information science and systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Algorithmic Prognostication in Female Oncofertility Counseling: Ethical Challenges of Bias, Autonomy, and Predictive Uncertainty.Healthcare (Basel, Switzerland) · 2026Review
- Using item response theory as a methodology to impute categorical missing values.Scientific reports · 2025Article
- Development of a Machine Learning Model for Predicting Treatment-Related Amenorrhea in Young Women with Breast Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development.International journal of environmental research and public health · 2021Article
- Support vector machine based aphasia classification of transcranial magnetic stimulation language mapping in brain tumor patients.NeuroImage. Clinical · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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. To this end, the reliability and robustness of six well-known imputation methods are evaluated. Our results show that imputation leads to a significant boost in the classification performance compared to the model prediction based on listwise deletion. Furthermore, the results reveal that most methods gain strong robustness and discriminant power even the dataset experiences high missing rate (> 50%).
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Registered trials
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