Combining data discretization and missing value imputation for incomplete medical datasets Article Swipe
Min‐Wei Huang
,
Chih‐Fong Tsai
,
Shu-Ching Tsui
,
Wei‐Chao Lin
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0295032
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0295032
Data discretization aims to transform a set of continuous features into discrete features, thus simplifying the representation of information and making it easier to understand, use, and explain. In practice, users can take advantage of the discretization process to improve knowledge discovery and data analysis on medical domain problem datasets containing continuous features. However, certain feature values were frequently missing. Many data-mining algorithms cannot handle incomplete datasets. In this study, we considered the use of both discretization and missing-value imputation to process incomplete medical datasets, examining how the order of discretization and missing-value imputation combined influenced performance. The experimental results were obtained using seven different medical domain problem datasets: two discretizers, including the minimum description length principle (MDLP) and ChiMerge; three imputation methods, including the mean/mode, classification and regression tree (CART), and k-nearest neighbor (KNN) methods; and two classifiers, including support vector machines (SVM) and the C4.5 decision tree. The results show that a better performance can be obtained by first performing discretization followed by imputation, rather than vice versa. Furthermore, the highest classification accuracy rate was achieved by combining ChiMerge and KNN with SVM.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0295032
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295032&type=printable
- OA Status
- gold
- Cited By
- 5
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389165633
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- OpenAlex ID
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https://openalex.org/W4389165633Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0295032Digital Object Identifier
- Title
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Combining data discretization and missing value imputation for incomplete medical datasetsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-11-30Full publication date if available
- Authors
-
Min‐Wei Huang, Chih‐Fong Tsai, Shu-Ching Tsui, Wei‐Chao LinList of authors in order
- Landing page
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https://doi.org/10.1371/journal.pone.0295032Publisher landing page
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295032&type=printableDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0295032&type=printableDirect OA link when available
- Concepts
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Imputation (statistics), Discretization, Missing data, Computer science, Data mining, Support vector machine, Decision tree, Knowledge extraction, Artificial intelligence, Pattern recognition (psychology), Machine learning, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2025: 3, 2024: 2Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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