A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches Article Swipe
Saba Bashir
,
Irfan Ullah Khattak
,
Aihab Khan
,
Farhan Hassan Khan
,
Abdullah Gani
,
Muhammad Shiraz
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/8190814
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/8190814
Feature selection is the process of identifying the most relevant features from the given data having a large feature space. Microarray datasets are comprised of high‐quality features and very few samples of data. Feature selection is performed on such datasets to identify the optimal feature subset. The major goal of feature selection is to improve the accuracy by identifying a minimal feature subset. For this purpose, the proposed research focused on analyzing and identifying effective feature selection algorithms. A novel framework is proposed which utilizes different feature selection methods from filters, wrappers, and embedded algorithms. Furthermore, classification is then performed on selected features to classify the data using a support vector machine (SVM) classifier. Two publically available benchmark datasets are used, i.e., the Microarray dataset and the Cleveland Heart Disease dataset, for experimentation and analysis, and they are archived from the UCI data repository. The performance of SVM is analyzed using accuracy, sensitivity, specificity, and f‐measure. The accuracy of 94.45% and 91% is achieved on each dataset, respectively.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/8190814
- https://downloads.hindawi.com/journals/complexity/2022/8190814.pdf
- OA Status
- gold
- Cited By
- 47
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4290692635
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4290692635Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/8190814Digital Object Identifier
- Title
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A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded ApproachesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Saba Bashir, Irfan Ullah Khattak, Aihab Khan, Farhan Hassan Khan, Abdullah Gani, Muhammad ShirazList of authors in order
- Landing page
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https://doi.org/10.1155/2022/8190814Publisher landing page
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https://downloads.hindawi.com/journals/complexity/2022/8190814.pdfDirect 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://downloads.hindawi.com/journals/complexity/2022/8190814.pdfDirect OA link when available
- Concepts
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Feature selection, Computer science, Selection (genetic algorithm), Artificial intelligence, Feature (linguistics), Pattern recognition (psychology), Data mining, Machine learning, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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47Total citation count in OpenAlex
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2025: 22, 2024: 14, 2023: 9, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1674043708, https://openalex.org/W2037803098, https://openalex.org/W1992209062, https://openalex.org/W2094372014, https://openalex.org/W2148910374, https://openalex.org/W1974395038, https://openalex.org/W2018296348, https://openalex.org/W2019576938, https://openalex.org/W2062302861, https://openalex.org/W2204950599, https://openalex.org/W1722431364, https://openalex.org/W2964138195, https://openalex.org/W2802707468, https://openalex.org/W2581109417, https://openalex.org/W2522701101, https://openalex.org/W172912734, https://openalex.org/W2955717168, https://openalex.org/W3121216533, https://openalex.org/W2886464381, https://openalex.org/W2973135033, https://openalex.org/W2848154351, https://openalex.org/W2922256990, https://openalex.org/W2755940713, https://openalex.org/W2521029800, https://openalex.org/W2949854251, https://openalex.org/W2585385891, https://openalex.org/W2563603481, https://openalex.org/W2143043751, https://openalex.org/W14070459, https://openalex.org/W323404752, https://openalex.org/W2014802425, https://openalex.org/W2142594886, https://openalex.org/W2899274556, https://openalex.org/W2559137209, https://openalex.org/W2785172785, https://openalex.org/W2796337804, https://openalex.org/W2768045609, https://openalex.org/W2766791857, https://openalex.org/W3086548118, https://openalex.org/W3117528838, https://openalex.org/W3024333932, https://openalex.org/W2752320209, https://openalex.org/W1680622244, https://openalex.org/W2517563311, https://openalex.org/W2096451472, https://openalex.org/W3125257807, https://openalex.org/W1922017469, https://openalex.org/W3101080475 |
| referenced_works_count | 48 |
| abstract_inverted_index.A | 78 |
| abstract_inverted_index.a | 16, 59, 108 |
| abstract_inverted_index.by | 57 |
| abstract_inverted_index.is | 2, 35, 52, 81, 97, 148, 162 |
| abstract_inverted_index.of | 5, 24, 31, 49, 146, 158 |
| abstract_inverted_index.on | 37, 70, 100, 164 |
| abstract_inverted_index.to | 40, 53, 103 |
| abstract_inverted_index.91% | 161 |
| abstract_inverted_index.For | 63 |
| abstract_inverted_index.SVM | 147 |
| abstract_inverted_index.The | 46, 144, 156 |
| abstract_inverted_index.Two | 114 |
| abstract_inverted_index.UCI | 141 |
| abstract_inverted_index.and | 27, 72, 92, 125, 133, 135, 154, 160 |
| abstract_inverted_index.are | 22, 119, 137 |
| abstract_inverted_index.few | 29 |
| abstract_inverted_index.for | 131 |
| abstract_inverted_index.the | 3, 7, 12, 42, 55, 66, 105, 122, 126, 140 |
| abstract_inverted_index.data | 14, 106, 142 |
| abstract_inverted_index.each | 165 |
| abstract_inverted_index.from | 11, 89, 139 |
| abstract_inverted_index.goal | 48 |
| abstract_inverted_index.most | 8 |
| abstract_inverted_index.such | 38 |
| abstract_inverted_index.then | 98 |
| abstract_inverted_index.they | 136 |
| abstract_inverted_index.this | 64 |
| abstract_inverted_index.very | 28 |
| abstract_inverted_index.(SVM) | 112 |
| abstract_inverted_index.Heart | 128 |
| abstract_inverted_index.data. | 32 |
| abstract_inverted_index.given | 13 |
| abstract_inverted_index.i.e., | 121 |
| abstract_inverted_index.large | 17 |
| abstract_inverted_index.major | 47 |
| abstract_inverted_index.novel | 79 |
| abstract_inverted_index.used, | 120 |
| abstract_inverted_index.using | 107, 150 |
| abstract_inverted_index.which | 83 |
| abstract_inverted_index.94.45% | 159 |
| abstract_inverted_index.having | 15 |
| abstract_inverted_index.space. | 19 |
| abstract_inverted_index.vector | 110 |
| abstract_inverted_index.Disease | 129 |
| abstract_inverted_index.Feature | 0, 33 |
| abstract_inverted_index.dataset | 124 |
| abstract_inverted_index.feature | 18, 44, 50, 61, 75, 86 |
| abstract_inverted_index.focused | 69 |
| abstract_inverted_index.improve | 54 |
| abstract_inverted_index.machine | 111 |
| abstract_inverted_index.methods | 88 |
| abstract_inverted_index.minimal | 60 |
| abstract_inverted_index.optimal | 43 |
| abstract_inverted_index.process | 4 |
| abstract_inverted_index.samples | 30 |
| abstract_inverted_index.subset. | 45, 62 |
| abstract_inverted_index.support | 109 |
| abstract_inverted_index.accuracy | 56, 157 |
| abstract_inverted_index.achieved | 163 |
| abstract_inverted_index.analyzed | 149 |
| abstract_inverted_index.archived | 138 |
| abstract_inverted_index.classify | 104 |
| abstract_inverted_index.dataset, | 130, 166 |
| abstract_inverted_index.datasets | 21, 39, 118 |
| abstract_inverted_index.embedded | 93 |
| abstract_inverted_index.features | 10, 26, 102 |
| abstract_inverted_index.filters, | 90 |
| abstract_inverted_index.identify | 41 |
| abstract_inverted_index.proposed | 67, 82 |
| abstract_inverted_index.purpose, | 65 |
| abstract_inverted_index.relevant | 9 |
| abstract_inverted_index.research | 68 |
| abstract_inverted_index.selected | 101 |
| abstract_inverted_index.utilizes | 84 |
| abstract_inverted_index.Cleveland | 127 |
| abstract_inverted_index.accuracy, | 151 |
| abstract_inverted_index.analysis, | 134 |
| abstract_inverted_index.analyzing | 71 |
| abstract_inverted_index.available | 116 |
| abstract_inverted_index.benchmark | 117 |
| abstract_inverted_index.comprised | 23 |
| abstract_inverted_index.different | 85 |
| abstract_inverted_index.effective | 74 |
| abstract_inverted_index.framework | 80 |
| abstract_inverted_index.performed | 36, 99 |
| abstract_inverted_index.selection | 1, 34, 51, 76, 87 |
| abstract_inverted_index.wrappers, | 91 |
| abstract_inverted_index.Microarray | 20, 123 |
| abstract_inverted_index.publically | 115 |
| abstract_inverted_index.algorithms. | 77, 94 |
| abstract_inverted_index.classifier. | 113 |
| abstract_inverted_index.identifying | 6, 58, 73 |
| abstract_inverted_index.performance | 145 |
| abstract_inverted_index.repository. | 143 |
| abstract_inverted_index.Furthermore, | 95 |
| abstract_inverted_index.f‐measure. | 155 |
| abstract_inverted_index.sensitivity, | 152 |
| abstract_inverted_index.specificity, | 153 |
| abstract_inverted_index.respectively. | 167 |
| abstract_inverted_index.classification | 96 |
| abstract_inverted_index.high‐quality | 25 |
| abstract_inverted_index.experimentation | 132 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5004063630, https://openalex.org/A5091401182 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I161371597, https://openalex.org/I40597779 |
| citation_normalized_percentile.value | 0.98442878 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |