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

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 provid...

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Bibliographic Details
Published in:Health information science and systems 2019-10, Vol.7 (1), p.1-8, Article 19
Main Authors: Wu, Xuetong, Akbarzadeh Khorshidi, Hadi, Aickelin, Uwe, Edib, Zobaida, Peate, Michelle
Format: Article
Language:English
Subjects:
Bioinformatics
Breast cancer
Care and treatment
Chemotherapy
Computational Biology/Bioinformatics
Computer Science
Data mining
Datasets
Deletion
Electronic records
Fertility
Health Informatics
Information Systems and Communication Service
Machine learning
Management
Medical records
Missing data
Performance degradation
Reliability analysis
Robustness
Special Issue on Artificial Intelligence in Health Informatics
Statistical analysis
Statistical methods
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Summary: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%).
ISSN:2047-2501
2047-2501
DOI:10.1007/s13755-019-0082-4