RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection
Abstract Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-04-01
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| Series: | SN Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-023-05339-2 |
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| _version_ | 1850253736001142784 |
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| author | Ismail Keshta Pallavi Sagar Deshpande Mohammad Shabaz Mukesh Soni Mohit kumar Bhadla Yasser Muhammed |
| author_facet | Ismail Keshta Pallavi Sagar Deshpande Mohammad Shabaz Mukesh Soni Mohit kumar Bhadla Yasser Muhammed |
| author_sort | Ismail Keshta |
| collection | DOAJ |
| description | Abstract Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained; in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced. |
| format | Article |
| id | doaj-art-ad1c6a45c01e48aaa3a0d8c67c9e9dd3 |
| institution | OA Journals |
| issn | 2523-3963 2523-3971 |
| language | English |
| publishDate | 2023-04-01 |
| publisher | Springer |
| record_format | Article |
| series | SN Applied Sciences |
| spelling | doaj-art-ad1c6a45c01e48aaa3a0d8c67c9e9dd32025-08-20T01:57:19ZengSpringerSN Applied Sciences2523-39632523-39712023-04-015511210.1007/s42452-023-05339-2RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detectionIsmail Keshta0Pallavi Sagar Deshpande1Mohammad Shabaz2Mukesh Soni3Mohit kumar Bhadla4Yasser Muhammed5Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa UniversityBharati Vidyapeeth (Deemed to Be University) College of EngineeringArba Minch UniversityDepartment of CSE, University Centre for Research & Development Chandigarh UniversityDepartment of Information Technology, Ahmedabad Institute of TechnologyCollege of Technical Engineering, Al-Farahidi UniversityAbstract Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained; in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced.https://doi.org/10.1007/s42452-023-05339-2Artificial intelligenceCancer DetectionFeature SelectionBiomedical ImageDeep Feature SelectionMachine Learning |
| spellingShingle | Ismail Keshta Pallavi Sagar Deshpande Mohammad Shabaz Mukesh Soni Mohit kumar Bhadla Yasser Muhammed RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection SN Applied Sciences Artificial intelligence Cancer Detection Feature Selection Biomedical Image Deep Feature Selection Machine Learning |
| title | RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection |
| title_full | RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection |
| title_fullStr | RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection |
| title_full_unstemmed | RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection |
| title_short | RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection |
| title_sort | retracted article multi stage biomedical feature selection extraction algorithm for cancer detection |
| topic | Artificial intelligence Cancer Detection Feature Selection Biomedical Image Deep Feature Selection Machine Learning |
| url | https://doi.org/10.1007/s42452-023-05339-2 |
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