Comprehensive Review of Hybrid Feature Selection Methods for Microarray-Based Cancer Detection Article Swipe
Azka Khoirunnisa
,
Didit Adytia
,
Mustafa Mat Deris
,
Adiwijaya Adiwijaya
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3564706
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3564706
Microarray data-based cancer detection advances early diagnosis and personalized medicine by utilizing gene expression data to develop comprehensive cancer profiles, measuring thousands of genes simultaneously. However, the inherent high-dimensional nature of microarray data introduces substantial challenges in data analysis and interpretation. To resolve these issues, gene selection techniques such as filter, wrapper, and embedded methods have been implemented to remove irrelevant genes and reduce the dimensionality of the data. Even with such usefulness, these methods are bound to restrictive elements individually that could compromise the precision of cancer detection systems. More recently, the focus of research has shifted to hybrid approaches that merge several feature selection techniques to mitigate the weaknesses of one method while maximizing the strengths of others. This paper offers an extensive review on feature selection techniques for microarray data and focuses on evaluating the performance of different hybrid methods as an important research gap. The research assesses various combinations of Filter, Wrapper, and Embedded techniques to determine how such hybrid approaches enhance classification accuracy. Hybrid approaches, those that integrate several techniques, have the ability to enhance diagnostic accuracy as well as improve understanding at the biological level. This paper provides a comparative evaluation of hybrid feature selection methods to enhance microarray-based cancer classification. It aims to guide researchers in choosing appropriate strategies that optimize the dataset analysis.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3564706
- OA Status
- gold
- Cited By
- 1
- References
- 98
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409882988Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3564706Digital Object Identifier
- Title
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Comprehensive Review of Hybrid Feature Selection Methods for Microarray-Based Cancer DetectionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Azka Khoirunnisa, Didit Adytia, Mustafa Mat Deris, Adiwijaya AdiwijayaList of authors in order
- Landing page
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https://doi.org/10.1109/access.2025.3564706Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2025.3564706Direct OA link when available
- Concepts
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Feature selection, Computer science, Cancer detection, Selection (genetic algorithm), Artificial intelligence, Feature (linguistics), Pattern recognition (psychology), Cancer, Medicine, Internal medicine, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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98Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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